bluuebunny/crossref_metadata_2025 huggingface dataset:
Combining QuickSCAT wind data and Landsat ETM+ images to evaluate the offshore wind power resource of East Vietnam Sea
Authors: Tung Nguyen Xuan , Cuong Do Huy , Anh Bui Thi Bao , Nhan Nguyen Thi , Son Tran Quang
Published: June 2020

Since the East Vietnam Sea has an advantageous geographical location and rich natural resources, we can develop and manage islands and reefs in this region reasonably to declare national sovereignty. Based on 1096 scenes of QuikSCAT wind data of 2006–2009, wind power density at 10 m hight is calculated to evaluate wind energy resources of the East Vietnam Sea. With a combination of wind power density at 70 m hight calculated according to the power law of wind energy profile and reef flats extracted from 35 scenes of Landsat ETM+ images, installed wind power capacity of every island or reef is estimated to evaluate wind power generation of the East Vietnam Sea. We found that the wind power density ranges from levels 4–7, so that the wind energy can be well applied to wind power generation. The wind power density takes on a gradually increasing trend in seasons. Specifically, the wind power density is lower in spring and summer, whereas it is higher in autumn and winter. Among islands and reefs in the East Vietnam Sea, the installed wind power capacity of Hoang Sa archipelago is highest in general, the installed wind power capacity of Truong Sa archipelago is at the third level. The installed wind power capacity of Discovery Reef, Bombay Reef, Tree island, Lincoln island, Woody Island of Hoang Sa archipelago and Mariveles Reef, Ladd Reef, Petley Reef, Cornwallis South Reef of Truong Sa archipelago is relatively high, and wind power generation should be developed on these islands first.
Research on the stability of the 3D frame on coral foundation subjected to impact load
Authors: Hung Nguyen Thanh , Chung Nguyen Thai , Luong Hoang Xuan
Published: June 2020

This article presents an application of the finite element method (FEM) for the stability analysis of 3D frame (space bar system) on the coral foundation impacted by collision impulse. One-way joints between the rod and the coral foundation are described by the contact element. Numerical analysis shows the effect of some factors on the stability of the bar system on coral foundation. The results of this study can be used for stability analysis of the bar system on coral foundation subjected to sea wave load.
A numerical model for simulation of near-shore waves and wave induced currents using the depth-averaged non-hydrostatic shallow water equations with an improvement of wave energy dissipation
Authors: Hieu Phung Dang , Vinh Phan Ngoc
Published: June 2020

This study proposes a numerical model based on the depth-integrated non-hydrostatic shallow water equations with an improvement of wave breaking dissipation. Firstly, studies of parameter sensitivity were carried out using the proposed numerical model for simulation of wave breaking to understand the effects of the parameters of the breaking model on wave height distribution. The simulated results of wave height near the breaking point were very sensitive to the time duration parameter of wave breaking. The best value of the onset breaking parameter is around 0.3 for the non-hydrostatic shallow water model in the simulation of wave breaking. The numerical results agreed well with the published experimental data, which confirmed the applicability of the present model to the simulation of waves in near-shore areas.
Learners’ Perception of the Applicability of Pre-clinical Subjects to Clinical Training and Experience in Obafemi Awolowo University, Ile-Ife
Authors: Emma-Okon Beatrice , Omoniyi-Esan Ganiat , Eziyi Josephine , Ogundipe Olufemi
Published: June 2020

This article was migrated. The article was marked as recommended. Introduction:This study investigated how Final Year Students and Newly qualified Doctors of Obafemi Awolowo University (OAU) Medical School perceive the applicability of the three pre-clinical subjects (Anatomy, Biochemistry and Physiology) to clinical studies and medical practice with the aim of ascertaining whether or not the students are able to establish a real connect between knowledge acquired in the pre-clinical years and their experience during clinical exposure. Methods:A cross sectional study involving 106 final year medical students and 81 interns was conducted using convenience sampling method. Data was collected using a structured self-administered questionnaire consisting of demographic characteristics and 10 items regarding perception of the relevance of basic medical courses to clinical training experience. Responses were rated using a 5 point Likert scale, which ranged from "strongly disagree" to "strongly agree". Data analysis was carried out using descriptive and inferential statistics and values expressed as mean of scores. Results:Overall mean scores for anatomy, biochemistry and physiology were 26.4 ± 0.32, 25.7 ± 0.29 and 28.1 ± 0.31 respectively. Thus, physiology was rated as having the highest applicability with the highest score in 4 out of 10 items. Biochemistry had the lowest score for the item "I remember most of the content of the course even now". Newly qualified Doctors were found to have a more positive perception of anatomy and biochemistry than final year medical students. All Subjects were scored high (average of 3-0/4.0) on items having to do with introduction of curriculum integration and clinical studies to teaching. Conclusion:Findings from the study suggest that the responses of the subjects were based more on their ability to understand and recall content, rather than how relevant the subjects are to their clinical experience. The study concludes that there is an urgent need to embrace integration of the curriculum and introduce more learner-centered teaching methods.
Review of purification methods of municipal solid waste landfills leachate
Authors: Milyutina Natalia , Politaeva Natalia , Zelenkovskii Pavel , Podlipskiy Ivan , Velikoselskaya Elena
Published: June 2020

The present article considers the characteristics of the chemical composition of municipal solid waste (MSW) landfills leachate. It also focuses on the need for leachate purification as leachate migrates outside the territory of the landfills impacting negatively environmental objects (soils, surface and underground waters).
The authors provide a literature review of different wastewater treatment methods: mechanical, biological and physicochemical. It is shown that each method aims at purification from specific pollutants, for example: organic impurities are removed by biological method using the potential of the vital activity of microorganisms; physicochemical methods such as precipitation, ion exchange, evaporation, reverse osmosis are used for the removal of heavy metals ions, salts cations and anions; water disinfection is carried out using chlorination, ozonation or ultraviolet radiation. Due to the presence of an extensive list of pollutants in the leachate as well as its variability depending on the stage of the landfill life cycle, complex leachate treatment technologies are used including a combination of the described methods for wastewater treatment. The article discusses schemes of functioning leachate treatment plants at MSW landfills in Russia and abroad.
Despite the successful use of a number of treatment plants, the authors examined the main disadvantages of the currently used technologies. During leachate purification, there is a production of secondary waste (up to 50 %) which must be disposed of. It is important to point out that the use of biological methods is associated with the need of maintaining the appropriate conditions for the growth of microorganisms and the use of large areas. Also, the use of physicochemical methods is related to a large consumption of chemicals, some of which can be harmful for health.
Modern trends in training specialists in the field of engineering
Authors: Lyalin Aleksej , Zozulya Anton , Eremina Tatyana , Zozulya Pavel
Published: June 2020

Companies are poorly satisfied with the level of training of specialists and put forward a number of requirements for specific professional competencies of the applicant in the field of engineering. Building a model of such a specialist is complicated by the fact that engineering services are not part of an independent branch of the national economy of Russia.
The training of University students is regulated by Federal state educational standards. For specialists in the field of engineering, it is necessary to involve representatives of the professional community in the development of Professional standards. This will allow us to offer outstanding professional competence in the engineering field, which will meet modern market needs and take into account the experience of the best world practices.
Modern specialists in the field of engineering should have a balanced combination of practical programs with traditional training, allowing students to apply their knowledge in real conditions. World experience shows another feature of training these specialists, which is aimed at developing non-technical skills in addition to mastering the basic curriculum in order to professionally succeed. In many respects, the effectiveness of applicants in the engineering field will be determined by their knowledge and skills in the organization and management of key stages of the life cycle of their projects.
The state University of management shares its experience in training project-oriented specialists in the field of management and engineering. At the University, the training program is based on engineering education using modern training technologies, methods and tools of project management, as well as direct communication between students and teachers. Active implementation of digital technologies improves academic mobility and contributes to increasing the share of the online segment of educational services.
Escalas de poder, instituições e terrotórios: estratégias orientadas ao desenvolvimento
Authors: Brandão Carlos Antônio
Published: June 2020

Resumo Este ensaio apresenta, sucintamente, as abordagens tradicionais e as interpretações modernas que tratam da temática das escalas espaciais do processo de desenvolvimento. Procura apontar que essas abordagens, com diferentes visões do que seja o local, acabam orientando intervenções públicas e privadas de natureza bastante distinta. A maioria delas são tentativas de elaboração teórica com pretensão de explicar situações bastante diversas, histórica e geograficamente. Discute-se como o tratamento teórico e o analítico articulador das temáticas das instituições e dos territórios devem ser inspirados por uma concepção de contexto e escala espacial construída política e socialmente. Identificam-se alguns desafios colocados por ambientes de exacerbadas heterogeneidades estruturais, como os da situação de atraso estrutural e de subdesenvolvimento do Brasil. Ao final, explicita uma interpretação à questão das possíveis alternativas e estratégias de desenvolvimento socioeconômico, procurando discutir a problemática da "escala adequada" à construção e à ação das instituições.
Teorias do capital social e desenvolvimento local: lições a partir da experiência de Pintadas (Bahia, Brasil)
Authors: Milani Carlos
Published: June 2020

Resumo Quais são as premissas e a origem da teoria do capital social? O que a análise crítica H J do conceito de capital social pode trazer de novo ao campo do desenvolvimento local. O conceito de capital social representa avanço epistemológico na tentativa atual de construir novas categorias de análise para ler e explicar a realidade do desenvolvimento local? Qual o valor heurístico deste conceito? Neste artigo, em primeiro lugar, expo os e analisamos as experiências de desenvolvimento local no Município de Pintadas (Bahia, Brasil). Buscamos, neste estudo de caso, significados (práticas e expressões) do capital social no projeto de desenvolvimento local de Pintadas que nos permitam explicar, de modo contextualizado e empiricamente analisado, relações possíveis entre tais significados e o projeto de transformação social (em seu discurso e em seus resultados). Em um segundo momento, a partir de uma revisão preliminar de alguns estudos sobre capital social, tentamos lançar primeiras interrogações acerca da importância deste conceito para compreender as estruturas de poder local e para analisar o desenvolvimento local em sua complexidade.
Desentrlização, intersetorialidade e rede na gestão da cidade
Authors: Junqueira Luciano A. Prates
Published: June 2020

Resumo A descentralização, intersetorialidade e rede são abordadas como estratégias de um novo modelo de gestão municipal na gestão das políticas sociais, tendo em vista a qualidade de vida dos munícipes. A descentralização, enquanto propõe a transferência de poder de decisão sobre as políticas sociais para os níveis periféricos da cidade, possibilita que a sua gestão ocorra com o envolvimento dos seus usuários. Essa transferência de poder, necessariamente, não otimiza a gestão integrada dessas políticas. A intersetorialidade constitui uma alternativa para otimizar essa gestão, pois integra as diversas políticas sociais na solução dos problemas sociais, que afetam grupos de população que ocupam determinado território. A rede, como a construção de relações entre atores sociais, preservando sua identidade, viabiliza e otimiza recursos necessários ao encaminhamento da gestão das políticas sociais. Analisa-se dois casos de modelos de gestão, no Brasil, a partir dos pressupostos, a experiência da reforma administrativa de Fortaleza, estado do Ceará, e a criação de uma rede intermunicipal de municípios da região de saúde de São João da Boa Vista, no estado de São Paulo.
O terceiro setor e a cidade: impasses, desafios e perspectivas de ação de ongs no poder local
Authors: Teodósio Armindo dos Santos de Sousa
Published: June 2020

Resumo O estudo procura discutir os impasses, desafios e perspectivas de atuação de organizações do Terceiro Setor na implementação de políticas sociais, no âmbito do Poder Local. A discussão volta-se para a delimitação terminológica do conceito de Terceiro Setor, através da co1strução de uma tipologia das diferentes organizações, movimentos e atores sociais que o compõem. Além disso, analisa-se o impacto do Terceiro Setor como espaço para renovação da gestão pública, discutindo-se sua relação com conceitos, tendências e práticas como participação popular, descentralização de políticas, accountabifity e conselhos populares. Procura-se levantar as estratégias de ação desenvolvidas por organizações do Terceiro Setor na implementação de bens e serviços sociais, com destaque para sua articulação com o Estado e organismos privados e/ou internacionais, financiadores de projetos sociais. São discutidos, também, os impasses e perspectivas de construção de modelos gerenciais em organizações não-governamentais, em especial a sua relação com a forma de ação do movimento social e os impactos sobre a gestão de mão-de-obra (voluntária e remunerada).
Desenvolvimento sustentável e trabalho precário no relato integrado da Natura: pensando um contrarrelato
Authors: Homero Junior Paulo Frederico , Carrieri Alexandre de Pádua
Published: June 2020

Resumo A incorporação do(s) conceito(s) de sustentabilidade aos discursos empresariais tem se caracterizado como um movimento ideológico voltado à legitimação do poder das grandes corporações. Em 2010 o International Integrated Reporting Committee (IIRC) foi organizado com o propósito de criar uma diretriz globalmente aceita para a contabilização da sustentabilidade. Como as teorias da legitimidade apontam que a comunicação desempenha um papel central nos processos de legitimação, neste artigo comparamos a representação da força de trabalho das revendedoras de cosméticos nos relatórios corporativos de 2015 da Natura Cosméticos S/A, uma das corporações brasileiras mais engajadas nas iniciativas do IIRC, aos resultados de pesquisas acadêmicas independentes. Com isso propomos um contrarrelato, ou seja, um discurso a respeito do desempenho organizacional produzido por atores que sejam independentes da gestão da organização. Corroborando o entendimento do IIRC como um empreendimento interorganizacional para legitimar uma definição de sustentabilidade que seja amigável aos negócios, argumentamos que, em seu relato integrado, a Natura celebra a exploração de trabalho precário como uma contribuição para o desenvolvimento sustentável.
Práticas e mecanismos de uma tecnologia social: proposição de um modelo a partir de uma experiência no semiárido
Authors: Souza Ana Clara Aparecida Alves de , Pozzebon Marlei
Published: June 2020

Resumo As crescentes desigualdades sociais, que desencadeiam e acentuam a exclusão social e a pobreza, têm reforçado a relevância da inovação social para a formulação de respostas alternativas ao que tem sido oferecido em modelos consolidados pelo status quo. Esta pesquisa considera a inovação social através da lente da tecnologia social, um conceito desenvolvido e documentado por pesquisadores latinoamericanos. Considerando a relevância das tecnologias sociais para a transformação de cenários sociais desfavorecidos, este estudo busca oferecer uma contribuição central: a concepção de um modelo conceitual, de natureza processual, que permite a identificação de mecanismos-chave subjacentes à transformação promovida por uma tecnologia social. Os resultados foram produzidos a partir de um estudo de caso único e em profundidade de uma organização da sociedade civil de interesse público que atua no semiárido cearense e vem promovendo transformações sociais na região, sobretudo contribuindo para a redução do êxodo de jovens por meio do fomento ao empreendedorismo rural. Como resultado do estudo, operacionalizamos um modelo conceitual para teorizar tecnologias sociais que contribui para uma melhor compreensão de seus mecanismos de funcionamento e modos de ação.
Reflections on work in the contemporary context of the creative economy
Authors: Haubrich Gislene Feiten , Bessi Vânia Gisele , Bohnenberger Maria Cristina , Freitas Ernani Cesar de
Published: June 2020

Abstract This article aims to reflect upon the concept of work in the field of creative economy, as well as its contribution to the understanding of the culture of these kinds of organizations in the contemporary context. It is an exploratory study, based on bibliographic and documentary research. The theoretical framework is grounded on the notions of work, with emphasis on immaterial work (Lazzarato & Negri, 2001; Schwartz & Durrive, 2015), rhizome (Deleuze & Guattari, 2000) and symbolic power (Bourdieu, 2002) to reflect on the context of creative economy (Florida, 2011; Howkins, 2013). As a main result of this initial reflection, it is understood that the interactions that are expected for and by creative economy workers move between a rhizomatic and libertarian perspective and a structural one based on shared values. In this case, organizations assume a different configuration through the culture expressed by norms and symbolic disputes, incorporated by each group in the conduct of their projects.
Samarco e o rompimento na barragem de Fundão: a narrativa como um recurso performativo da prática estratégica de sensegiving inerente ao strategizing pós-desastre
Authors: Ceni Jéssica Cristina , Rese Natália
Published: June 2020

Resumo Esta pesquisa possui como objetivo analisar como ocorreu o processo de sensegiving nas narrativas da Samarco, como uma prática estratégica, após o rompimento na barragem de Fundão. Para isso, desenvolveu-se uma pesquisa qualitativa, com base em dados secundários, sendo coletados 307 arquivos (893 páginas) no site da Samarco, 297 arquivos (647 páginas) no site da Fundação Renova e 52 vídeos (191 minutos) no canal do Youtube Samarco Mineração. Em primeiro momento foi realizada uma análise de conteúdo cujo objetivo era observar os direcionamentos de conteúdo dado pela empresa a partir do rompimento; dessa análise foram inferidos seis momentos narrativos. Foi observado, a partir da análise narrativa, que a empresa aderiu desde o rompimento a uma estratégia discursiva que apresenta um sensegiving direcionado à volta de suas operações.
Um arranjo produtivo local sob a perspectiva da teoria da regulação cultural: o caso do Porto Digital englobando o Portomídia
Authors: Silva Anderson Diego Farias da , Fernandes Nelson da Cruz Monteiro , Paiva Júnior Fernando Gomes de
Published: June 2020

Resumo Este estudo objetiva desvelar o modo como ocorre o processo de regulação cultural na ampliação do núcleo de tecnologia da informação e comunicação Porto Digital, domiciliando um polo de economia criativa denominado Portomídia -Centro de Empreendedorismo e Tecnologia da Economia Criativa. Sob o prisma dos estudos culturais, a partir da teoria da regulação cultural, buscamos compreender os significados articulados para a expansão do escopo de atuação desse sistema tecnológico. Desenvolvemos um estudo de caso incorporado para avaliar o arranjo produtivo local Porto Digital a partir de diferentes níveis ou unidades de análise, envolvendo apreciações sobre a regulação cultural nos âmbitos do poder institucional, econômico e subjetivo. Essas categorias analíticas fundamentaram as análises que contemplam a ideia da influência do poder econômico (sistema de classes) e do poder institucional (regras e regulação) na (re)configuração dessa aglomeração de empresas intensivas em criatividade. Por sua vez, o poder subjetivo se manifesta no ato empreendedor como ação política fundamental para a expansão do polo criativo.
Significando o trabalho voluntário em casa de apoio oncológica
Authors: Salci Maria Aparecida , Casado Jéssica Manari , Wakiuchi Julia , Paiano Marcelle , Charlo Patrícia Bossolani , Sales Catarina Aparecida
Published: June 2020

RESUMO Objetivo Compreender o significado atribuído pelas pessoas que desenvolveram trabalho voluntário em serviço de apoio oncológico. Método Trata-se de estudo qualitativo, que teve como referencial teórico o Interacionismo Simbólico, realizado com dez voluntárias em uma casa de apoio oncológica em um Município de médio porte localizado no sul do Brasil. A coleta de dados ocorreu entre junho e agosto de 2016 por meio de entrevistas abertas, organizadas com auxilio do software IRAMUTEQ e realizado analise de conteúdo temática. Resultados Como resultados, emergiram três categorias que discorrem sobre a motivação e o desejo de dedicar-se ao outro, os entraves no trabalho voluntario e o crescimento e satisfação pessoal na doação ao outro. Conclusão e implicações para a prática O trabalho voluntário envolve questões culturais, crenças, caridade e amor ao próximo, mas pode gerar sentimentos de sofrimento e impotência além de dificuldades financeiras para sua execução. Apesar das dificuldades encontradas, propicia reconhecimento pessoal e profissional a quem o realiza.
Sistematização da assistência de enfermagem: a práxis do enfermeiro de hospital de pequeno porte
Authors: Barreto Mayckel da Silva , Prado Eleandro do , Lucena Ane Caroline Rodrigues Miranda , Rissardo Leydiani Karina , Furlan Mara Cristina Ribeiro , Marcon Sonia Silva
Published: June 2020

RESUMO Objetivo descrever a vivência dos enfermeiros atuantes em unidade hospitalar em relação à Sistematização da Assistência de Enfermagem (SAE). Método estudo descritivo, de abordagem qualitativa, realizado em dois hospitais de pequeno porte de um município do extremo noroeste do Paraná. A coleta de dados ocorreu entre maio e junho de 2018, por meio de entrevistas abertas e audiogravadas, com 14 enfermeiros. Após transcrição na íntegra as entrevistas foram submetidas à Análise de Conteúdo, modalidade temática. Resultados Foram elaboradas duas categorias temáticas: “Dificuldades enfrentadas pelos enfermeiros para a aplicação da SAE” e “Estratégias empregadas para facilitar a aplicação da SAE no cotidiano da enfermagem”. Conclusão e implicações para a prática Aspectos relacionados à liderança, hierarquia, processo de trabalho e sensibilização dos enfermeiros acerca da SAE dificultaram sua aplicação. Em contrapartida, os enfermeiros apontaram que a utilização de instrumentos padronizados, o treinamento da equipe de enfermagem e o apoio dos gestores hospitalares são imprescindíveis para a aplicação da SAE. Esses achados mostram que ainda é preciso promover, na formação e no serviço, ações/estratégias que permitam aos enfermeiros se apoderarem e aplicarem a SAE no contexto hospitalar.
Regulación de la Enfermería Española: incorporación a la profesión sanitaria (1850-1950)
Authors: Gómez-Cantarino Sagrario , Dios-Aguado Mercedes de , Peñalver Abel Checa , Dominguez-Isabel Patricia , Montejano Jose Rodríguez , Espina-Jerez Blanca
Published: June 2020

RESUMEN OBJETIVO Conocer la evolución de la formación académica de la enfermería española (1850-1950), describiendo los cambios que la transformaron en profesión sanitaria. MÉTODO Investigación histórica en la modalidad exploratoria-descriptiva, con análisis e interpretación de información obtenida en bases de datos electrónicas, archivos nacionales, provinciales y municipales, Boletines Oficiales y Bibliotecas. RESULTADOS En 1850 coexistían diferentes categorías sanitarias, sin embargo, el título de enfermera no surgió hasta 1915. Con el auge que alcanzó la Salud Comunitaria durante esa época, en 1924, se funda la Escuela Nacional de Sanidad, creándose en 1932 las especialidades de Enfermero Psiquiátrico, Enfermeras Pediátricas y Enfermeras Visitadoras. CONCLUSIONES E IMPLICACIÓN PARA LA PRÁCTICA Entre 1915 y 1950 los profesionales de enfermería poseían formación universitaria, por tanto, a través del conocimiento científico estos sanitarios adquirieron una identidad propia dentro de las profesiones de la salud. Se logró proporcionar una asistencia especializada para el cuidado de personas.
MATHEMATICS IN CLASSROMS, NA ICONOGRAPHIC HISTORICAL STUDY
Authors: Matos José Manuel , Rodrigues Alexandra Sofia
Published: June 2020

Abstract Historically studying representations of math-centered classrooms shows us arrangements revealing underlying teaching methodologies, learning theories, and technological innovations. This iconographic study draws on images in textbooks, magazines or collectors' collections from medieval times to the mid-twentieth century. Using an interpretative paradigm, involving historical and documentary research, we look for the perspectives of teaching and learning mathematics.
Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks
Authors: Liu Qianhui , Ruan Haibo , Xing Dong , Tang Huajin , Pan Gang
Published: June 2020

Address event representation (AER) cameras have recently attracted more attention due to the advantages of high temporal resolution and low power consumption, compared with traditional frame-based cameras. Since AER cameras record the visual input as asynchronous discrete events, they are inherently suitable to coordinate with the spiking neural network (SNN), which is biologically plausible and energy-efficient on neuromorphic hardware. However, using SNN to perform the AER object classification is still challenging, due to the lack of effective learning algorithms for this new representation. To tackle this issue, we propose an AER object classification model using a novel segmented probability-maximization (SPA) learning algorithm. Technically, 1) the SPA learning algorithm iteratively maximizes the probability of the classes that samples belong to, in order to improve the reliability of neuron responses and effectiveness of learning; 2) a peak detection (PD) mechanism is introduced in SPA to locate informative time points segment by segment, based on which information within the whole event stream can be fully utilized by the learning. Extensive experimental results show that, compared to state-of-the-art methods, not only our model is more effective, but also it requires less information to reach a certain level of accuracy.
STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits
Authors: Bhattacharya Uttaran , Mittal Trisha , Chandra Rohan , Randhavane Tanmay , Bera Aniket , Manocha Dinesh
Published: June 2020

We present a novel classifier network called STEP, to classify perceived human emotion from gaits, based on a Spatial Temporal Graph Convolutional Network (ST-GCN) architecture. Given an RGB video of an individual walking, our formulation implicitly exploits the gait features to classify the perceived emotion of the human into one of four emotions: happy, sad, angry, or neutral. We train STEP on annotated real-world gait videos, augmented with annotated synthetic gaits generated using a novel generative network called STEP-Gen, built on an ST-GCN based Conditional Variational Autoencoder (CVAE). We incorporate a novel push-pull regularization loss in the CVAE formulation of STEP-Gen to generate realistic gaits and improve the classification accuracy of STEP. We also release a novel dataset (E-Gait), which consists of 4,227 human gaits annotated with perceived emotions along with thousands of synthetic gaits. In practice, STEP can learn the affective features and exhibits classification accuracy of 88% on E-Gait, which is 14–30% more accurate over prior methods.
Modeling Electrical Motor Dynamics Using Encoder-Decoder with Recurrent Skip Connection
Authors: Verma Sagar , Henwood Nicolas , Castella Marc , Malrait Francois , Pesquet Jean-Christophe
Published: June 2020

Electrical motors are the most important source of mechanical energy in the industrial world. Their modeling traditionally relies on a physics-based approach, which aims at taking their complex internal dynamics into account. In this paper, we explore the feasibility of modeling the dynamics of an electrical motor by following a data-driven approach, which uses only its inputs and outputs and does not make any assumption on its internal behaviour. We propose a novel encoder-decoder architecture which benefits from recurrent skip connections. We also propose a novel loss function that takes into account the complexity of electrical motor quantities and helps in avoiding model bias. We show that the proposed architecture can achieve a good learning performance on our high-frequency high-variance datasets. Two datasets are considered: the first one is generated using a simulator based on the physics of an induction motor and the second one is recorded from an industrial electrical motor. We benchmark our solution using variants of traditional neural networks like feedforward, convolutional, and recurrent networks. We evaluate various design choices of our architecture and compare it to the baselines. We show the domain adaptation capability of our model to learn dynamics just from simulated data by testing it on the raw sensor data. We finally show the effect of signal complexity on the proposed method ability to model temporal dynamics.
Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series
Authors: Xu Dongkuan , Cheng Wei , Zong Bo , Song Dongjin , Ni Jingchao , Yu Wenchao , Liu Yanchi , Chen Haifeng , Zhang Xiang
Published: June 2020

The problem of learning and forecasting underlying trends in time series data arises in a variety of applications, such as traffic management, energy optimization, etc. In literature, a trend in time series is characterized by the slope and duration, and its prediction is then to forecast the two values of the subsequent trend given historical data of the time series. For this problem, existing approaches mainly deal with the case in univariate time series. However, in many real-world applications, there are multiple variables at play, and handling all of them at the same time is crucial for an accurate prediction. A natural way is to employ multi-task learning (MTL) techniques in which the trend learning of each time series is treated as a task. The key point of MTL is to learn task relatedness to achieve better parameter sharing, which however is challenging in trend prediction task. First, effectively modeling the complex temporal patterns in different tasks is hard as the temporal and spatial dimensions are entangled. Second, the relatedness among tasks may change over time. In this paper, we propose a neural network, DeepTrends, for multivariate time series trend prediction. The core module of DeepTrends is a tensorized LSTM with adaptive shared memory (TLASM). TLASM employs the tensorized LSTM to model the temporal patterns of long-term trend sequences in an MTL setting. With an adaptive shared memory, TLASM is able to learn the relatedness among tasks adaptively, based upon which it can dynamically vary degrees of parameter sharing among tasks. To further consider short-term patterns, DeepTrends utilizes a multi-task 1dCNN to learn the local time series features, and employs a task-specific sub-network to learn a mixture of long-term and short-term patterns for trend prediction. Extensive experiments on real datasets demonstrate the effectiveness of the proposed model.
Improved Filtering for the Euclidean Traveling Salesperson Problem in CLP(FD)
Authors: Bertagnon Alessandro , Gavanelli Marco
Published: June 2020

The Traveling Salesperson Problem (TSP) is one of the best-known problems in computer science. The Euclidean TSP is a special case in which each node is identified by its coordinates on the plane and the Euclidean distance is used as cost function.

Many works in the Constraint Programming (CP) literature addressed the TSP, and use as benchmark Euclidean instances; however the usual approach is to build a distance matrix from the points coordinates, and then address the problem as a TSP, disregarding the information carried by the points coordinates for constraint propagation.

In this work, we propose to use geometric information, present in Euclidean TSP instances, to improve the filtering power. In order to have a declarative approach, we implemented the filtering algorithms in Constraint Logic Programming on Finite Domains (CLP(FD)).
Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems
Authors: Mandi Jayanta , Demirovi? Emir , Stuckey Peter J. , Guns Tias
Published: June 2020

Combinatorial optimization assumes that all parameters of the optimization problem, e.g. the weights in the objective function, are fixed. Often, these weights are mere estimates and increasingly machine learning techniques are used to for their estimation. Recently, Smart Predict and Optimize (SPO) has been proposed for problems with a linear objective function over the predictions, more specifically linear programming problems. It takes the regret of the predictions on the linear problem into account, by repeatedly solving it during learning. We investigate the use of SPO to solve more realistic discrete optimization problems. The main challenge is the repeated solving of the optimization problem. To this end, we investigate ways to relax the problem as well as warm-starting the learning and the solving. Our results show that even for discrete problems it often suffices to train by solving the relaxation in the SPO loss. Furthermore, this approach outperforms the state-of-the-art approach of Wilder, Dilkina, and Tambe. We experiment with weighted knapsack problems as well as complex scheduling problems, and show for the first time that a predict-and-optimize approach can successfully be used on large-scale combinatorial optimization problems.
Efficient Algorithms for Generating Provably Near-Optimal Cluster Descriptors for Explainability
Authors: Sambaturu Prathyush , Gupta Aparna , Davidson Ian , Ravi S. S. , Vullikanti Anil , Warren Andrew
Published: June 2020

Improving the explainability of the results from machine learning methods has become an important research goal. Here, we study the problem of making clusters more interpretable by extending a recent approach of [Davidson et al., NeurIPS 2018] for constructing succinct representations for clusters. Given a set of objects S, a partition π of S (into clusters), and a universe T of tags such that each element in S is associated with a subset of tags, the goal is to find a representative set of tags for each cluster such that those sets are pairwise-disjoint and the total size of all the representatives is minimized. Since this problem is NP-hard in general, we develop approximation algorithms with provable performance guarantees for the problem. We also show applications to explain clusters from datasets, including clusters of genomic sequences that represent different threat levels.
Multiagent Evaluation Mechanisms
Authors: Alon Tal , Dobson Magdalen , Procaccia Ariel , Talgam-Cohen Inbal , Tucker-Foltz Jamie
Published: June 2020

We consider settings where agents are evaluated based on observed features, and assume they seek to achieve feature values that bring about good evaluations. Our goal is to craft evaluation mechanisms that incentivize the agents to invest effort in desirable actions; a notable application is the design of course grading schemes. Previous work has studied this problem in the case of a single agent. By contrast, we investigate the general, multi-agent model, and provide a complete characterization of its computational complexity.
All-Pay Bidding Games on Graphs
Authors: Avni Guy , Ibsen-Jensen Rasmus , Tkadlec Josef
Published: June 2020

In this paper we introduce and study all-pay bidding games, a class of two player, zero-sum games on graphs. The game proceeds as follows. We place a token on some vertex in the graph and assign budgets to the two players. Each turn, each player submits a sealed legal bid (non-negative and below their remaining budget), which is deducted from their budget and the highest bidder moves the token onto an adjacent vertex. The game ends once a sink is reached, and Player 1 pays Player 2 the outcome that is associated with the sink. The players attempt to maximize their expected outcome. Our games model settings where effort (of no inherent value) needs to be invested in an ongoing and stateful manner. On the negative side, we show that even in simple games on DAGs, optimal strategies may require a distribution over bids with infinite support. A central quantity in bidding games is the ratio of the players budgets. On the positive side, we show a simple FPTAS for DAGs, that, for each budget ratio, outputs an approximation for the optimal strategy for that ratio. We also implement it, show that it performs well, and suggests interesting properties of these games. Then, given an outcome c, we show an algorithm for finding the necessary and sufficient initial ratio for guaranteeing outcome c with probability 1 and a strategy ensuring such. Finally, while the general case has not previously been studied, solving the specific game in which Player 1 wins iff he wins the first two auctions, has been long stated as an open question, which we solve.
VCG under Sybil (False-Name) Attacks - A Bayesian Analysis
Authors: Gafni Yotam , Lavi Ron , Tennenholtz Moshe
Published: June 2020

VCG is a classical combinatorial auction that maximizes social welfare. However, while the standard single-item Vickrey auction is false-name-proof, a major failure of multi-item VCG is its vulnerability to false-name attacks. This occurs already in the natural bare minimum model in which there are two identical items and bidders are single-minded. Previous solutions to this challenge focused on developing alternative mechanisms that compromise social welfare. We re-visit the VCG auction vulnerability and consider the bidder behavior in Bayesian settings. In service of that we introduce a novel notion, termed the granularity threshold, that characterizes VCG Bayesian resilience to false-name attacks as a function of the bidder type distribution. Using this notion we show a large class of cases in which VCG indeed obtains Bayesian resilience for the two-item single-minded setting.
A Simple, Fast, and Safe Mediator for Congestion Management
Authors: Ikegami Kei , Okumura Kyohei , Yoshikawa Takumi
Published: June 2020

Congestion is a severe problem in cities. A large population with little information about each other's preferences hardly reaches equilibrium and causes unexpected congestion. Controlling such congestion requires us to collect information dispersed in the market and to coordinate actions among agents. We aim to design a mediator that a) induces a game with high social welfare in equilibrium, b) computes an equilibrium efficiently, c) works without common prior, and d) performs well even when only some of the agents in the market use the mediator. We propose a mediator based on a version of best response dynamics (BRD). We prove that, in a simple setting with two resources, “good behavior” (reporting truthfully and following the recommendation) forms an (approximate) ex-post Nash equilibrium in the mediated game; in the equilibrium, the welfare is close to the first-best when preferences diverge enough. Furthermore, under a certain behavioral assumption, those who are not using the mediator can always enjoy non-negative payoff gain by joining it even without the full participation of others. Additionally, our experimental results suggest that such results remain valid for more general settings.
A Multiarmed Bandit Based Incentive Mechanism for a Subset Selection of Customers for Demand Response in Smart Grids
Authors: Shweta Jain , Sujit Gujar
Published: June 2020

Demand response is a crucial tool to maintain the stability of the smart grids. With the upcoming research trends in the area of electricity markets, it has become a possibility to design a dynamic pricing system, and consumers are made aware of what they are going to pay. Though the dynamic pricing system (pricing based on the total demand a distributor company is facing) seems to be one possible solution, the current dynamic pricing approaches are either too complex for a consumer to understand or are too naive leading to inefficiencies in the system (either consumer side or distributor side). Due to these limitations, the recent literature is focusing on the approach to provide incentives to the consumers to reduce the electricity, especially in peak hours. For each round, the goal is to select a subset of consumers to whom the distributor should offer incentives so as to minimize the loss which comprises of cost of buying the electricity from the market, uncertainties at consumer end, and cost incurred to the consumers to reduce the electricity which is a private information to the consumers. Due to the uncertainties in the loss function (arising from renewable energy resources as well as consumption needs), traditional auction theory-based incentives face manipulation challenges. Towards this, we propose a novel combinatorial multi-armed bandit (MAB) algorithm, which we refer to as \namemab\ to learn the uncertainties along with an auction to elicit true costs incurred by the consumers. We prove that our mechanism is regret optimal and is incentive compatible. We further demonstrate efficacy of our algorithms via simulations.
Deep Learning—Powered Iterative Combinatorial Auctions
Authors: Weissteiner Jakob , Seuken Sven
Published: June 2020

In this paper, we study the design of deep learning-powered iterative combinatorial auctions (ICAs). We build on prior work where preference elicitation was done via kernelized support vector regressions (SVRs). However, the SVR-based approach has limitations because it requires solving a machine learning (ML)-based winner determination problem (WDP). With expressive kernels (like gaussians), the ML-based WDP cannot be solved for large domains. While linear or quadratic kernels have better computational scalability, these kernels have limited expressiveness. In this work, we address these shortcomings by using deep neural networks (DNNs) instead of SVRs. We first show how the DNN-based WDP can be reformulated into a mixed integer program (MIP). Second, we experimentally compare the prediction performance of DNNs against SVRs. Third, we present experimental evaluations in two medium-sized domains which show that even ICAs based on relatively small-sized DNNs lead to higher economic efficiency than ICAs based on kernelized SVRs. Finally, we show that our DNN-powered ICA also scales well to very large CA domains.
Algorithms for Manipulating Sequential Allocation
Authors: Xiao Mingyu , Ling Jiaxing
Published: June 2020

Sequential allocation is a simple and widely studied mechanism to allocate indivisible items in turns to agents according to a pre-specified picking sequence of agents. At each turn, the current agent in the picking sequence picks its most preferred item among all items having not been allocated yet. This problem is well-known to be not strategyproof, i.e., an agent may get more utility by reporting an untruthful preference ranking of items. It arises the problem: how to find the best response of an agent? It is known that this problem is polynomially solvable for only two agents and NP-complete for an arbitrary number of agents. The computational complexity of this problem with three agents was left as an open problem. In this paper, we give a novel algorithm that solves the problem in polynomial time for each fixed number of agents. We also show that an agent can always get at least half of its optimal utility by simply using its truthful preference as the response.
Runtime Analysis of Somatic Contiguous Hypermutation Operators in MOEA/D Framework
Authors: Huang Zhengxin , Zhou Yuren
Published: June 2020

Somatic contiguous hypermutation (CHM) operators are important variation operators in artificial immune systems. The few existing theoretical studies are only concerned with understanding the optimization behavior of CHM operators on solving single-objective optimization problems. The MOEA/D framework is one of the most popular strategies for solving multi-objective optimization problems (MOPs). In this paper, we present a runtime analysis of using two CHM operators in MOEA/D framework for solving five benchmark MOPs, including four bi-objective and one many-objective problems. Our analyses show that the expected runtimes of CHM operators on the four bi-objective problems are better than or as good as that of the well-studied standard bit mutation operator. Moreover, using CHM operators in MOEA/D framework can improve the best known upper bound on the many-objective problem by a factor of n. This paper provides insight into understanding the optimization behavior of CHM operators in the well-known MOEA/D framework, and indicates that using the CHM operator in MOEA/D framework is a promising method for handling MOPs.
Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems
Authors: Khairy Sami , Shaydulin Ruslan , Cincio Lukasz , Alexeev Yuri , Balaprakash Prasanna
Published: June 2020

Quantum computing is a computational paradigm with the potential to outperform classical methods for a variety of problems. Proposed recently, the Quantum Approximate Optimization Algorithm (QAOA) is considered as one of the leading candidates for demonstrating quantum advantage in the near term. QAOA is a variational hybrid quantum-classical algorithm for approximately solving combinatorial optimization problems. The quality of the solution obtained by QAOA for a given problem instance depends on the performance of the classical optimizer used to optimize the variational parameters. In this paper, we formulate the problem of finding optimal QAOA parameters as a learning task in which the knowledge gained from solving training instances can be leveraged to find high-quality solutions for unseen test instances. To this end, we develop two machine-learning-based approaches. Our first approach adopts a reinforcement learning (RL) framework to learn a policy network to optimize QAOA circuits. Our second approach adopts a kernel density estimation (KDE) technique to learn a generative model of optimal QAOA parameters. In both approaches, the training procedure is performed on small-sized problem instances that can be simulated on a classical computer; yet the learned RL policy and the generative model can be used to efficiently solve larger problems. Extensive simulations using the IBM Qiskit Aer quantum circuit simulator demonstrate that our proposed RL- and KDE-based approaches reduce the optimality gap by factors up to 30.15 when compared with other commonly used off-the-shelf optimizers.