Machine translation and the disruption of foreign language learning activities

This study examines the question of how language teachers in a highly technology-friendly university environment view machine translation and the implications that this has for the personal learning environments of students. It brings an activity-theory perspective to the question, examining the ways that the introduction of new tools can disrupt the relationship between different elements in an activity system. This perspective opens up for an investigation of the ways that new tools have the potential to fundamentally alter traditional learning activities. In questionnaires and group discussions, respondents showed general agreement that although use of machine translation by students could be considered cheating, students are bound to use it anyway, and suggested that teachers focus on the kinds of skills students would need when using machine translation and design assignments and exams to practice and assess these skills. The results of the empirical study are used to reflect upon questions of what the roles of teachers and students are in a context where many of the skills that a person needs to be able to interact in a foreign language increasingly can be outsourced to laptops and smartphones.

Machine Learning Algorithms in Heavy Process Manufacturing

In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.

Modernist Success in a Postmodern Failure: Jackson Pollock and Abstract Expressionism, the Avant-Garde and the Ascension of Late Capitalism, Art after 1945

It is hard to imagine the magnitude of the events at the end of World War II. The thought produced in the face of a myriad of deaths is almost unfeasible sixty years after the fact, but the energy was integral to the changing social landscape. Because of the country's prominence in and fortitude after the war, the U.S. was left responsible for reshaping and rejuvenating the international landscape that was destroyed by the years of brutal fighting and vile contestation. The American establishment was granted a major opportunity to establish itself amongst the global leaders. Such a grand responsibility must account for the multiplicity of thought that arises in such a decisive moment. In order to align the Abstract Expressionist art movement with the intersection of the intense, multifaceted thought developed during the postwar period, the following will discuss the political, philosophical, economic, and art historical overlap that occurred in the mid to late 1940s in the hopes of illustrating the fertility yet lingering problems associated with the restructuring of the world with America at the helm. In this way, the duration of the Abstract Expressionist moment will be better understood for both its triumphs and downfalls.

Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.

Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the model output uncertainty which is specific for the new input data. We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties. These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to combine their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal. The verification results show that merged output is better than an individual model output. [1] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press, 2013.

Abstract types and distributions in independent private value auctions

In this note, in an independent private values auction framework, I discuss the relationship between the set of types and the distribution of types. I show that any set of types, finite dimensional or not, can be extended to a larger set of types preserving incentive compatibility constraints, expected revenue and bidder’s expected utilities. Thus for example we may convexify a set of types making our model amenable to the large body of theory in economics and mathematics that relies on convexity assumptions. An interesting application of this extension procedure is to show that although revenue equivalence is not valid in general if the set of types is not convex these mechanism have underlying distinct allocation mechanism in the extension. Thus we recover in these situations the revenue equivalence.

Do controle da legalidade às auditorias operacionais : os Tribunais de Contas e o controle financeiro da administração pública brasileira.

A tendência de ampliação do papel dos Tribunais de Contas no cenário político nacional através dos mecanismos de controle que lhes foram atribuídos pela Constituição Federal de 1988, antes limitada a uma simples verificação da legalidade dos atos dos gestores públicos, sua área de atuação, foi acrescida da capacidade de auditar a qualidade da gestão pública, visando, principalmente a economicidade, eficiência, eficácia e efetividade das políticas implementadas. Neste cenário, a auditoria operacional surge como uma ferramenta importante para que estes órgãos de controle possam exercer a missão de fiscalização dos gestores da res pública, de forma a garantir que estes conduzam a máquina pública sempre utilizando a política pública mais eficiente para a obtenção de resultados que sejam positivos para a sociedade. O objetivo deste trabalho é verificar como os Tribunais de Contas brasileiros estão lidando com a tarefa de fiscalizar as questões relativas à Auditoria Operacional, respondendo a duas perguntas básicas: a) Se as auditorias operacionais realizadas pelas entidades fiscalizadoras têm, efetivamente, contribuído para o alcance dos objetivos das políticas públicas, e; b) Se as técnicas até aqui utilizadas na execução das auditorias operacionais são adequadas para a avaliação dessas políticas. Em relação à primeira questão, concluímos que, da forma com que as auditorias operacionais estão sendo realizadas no Brasil, ainda há uma distância razoável a ser percorrida antes que se possa dizer que sim, devido, principalmente, a falta de uma determinação no sentido de responsabilizar nominalmente os responsáveis pela condução das recomendações expedidas pelos Tribunais de Contas, quando da publicação do acórdão que se origina dos trabalhos de auditoria. Quanto ao segundo questionamento, acreditamos que a realização de auditorias de natureza operacional engloba uma série de fatores que vão desde a dificuldade de se obter dos jurisdicionados indicadores que indiquem claramente quais os objetivos que se procurou atingir com determinada política, até a necessidade dos próprios tribunais de contas de instituírem quadros de pessoal específicos, voltados para esta tarefa.

Soil compaction by machine traffic and least limiting water range related to soybean yield

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Aquatic weed automatic classification using machine learning techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)