Os escorregamentos translacionais rasos são freqüentes na região da Serra do Mar, principalmente quando associados a eventos pluviométricos extremos, como em Janeiro de 1985 (380 mm, em 2 dias). Quando deflagrados de forma generalizada, podem ser catastróficos causando danos para a sociedade. Inúmeros métodos vêm sendo propostos para compreender a ocorrência destes processos na paisagem. O objetivo deste trabalho é avaliar a suscetibilidade a escorregamentos translacionais rasos na Serra do Mar por meio da aplicação de um modelo matemático em bases físicas, tendo como resultado um índice de estabilidade, que aponta, em forma de perigo relativo, áreas passíveis de instabilizações. As etapas de trabalho dividiramse na construção do Modelo Digital de Terreno e em produtos derivados (ângulo da encosta, curvatura, aspecto e área de contribuição), no mapeamento das cicatrizes de 1985 e na simulação dos cenários de suscetibilidade. Os mapas dos parâmetros topográficos, assim como, os mapas de suscetibilidade foram correlacionados com o mapa de cicatrizes e avaliados utilizando-se dos índices de Concentração de Cicatrizes (CC) e Potencial de Escorregamento (PE). Foram mapeadas 216 cicatrizes para uma área de 2,5 km² com uma produção de sedimentos estimado em 135,525m³. Os resultados apontam que encostas com ângulos entre 30° e 40° e com formato retilíneo foram as mais suscetíveis. A área foi considerada instável, segundo o modelo, em todos os cenários, tendo a melhor calibração para o cenário C2. O emprego de diferentes métodos demonstrou-se bastante satisfatório e concordante na análise do resultado final. Além disso, podem auxiliar como ferramentas de apoio de decisão no planejamento do uso do solo, principalmente em regiões onde é freqüente a ocorrência de movimentos de massa. Portanto, o resultado da avaliação a susceptibilidade a escorregamentos rasos na Serra do Mar pode direcionar ações mitigadoras político-administrativas e ambientais, tendo em vista minimizar o impacto sócio-ambiental de eventos futuros. / The shallow landslides are frequent in the Serra do Mar, especially when associated with intense rainfall events, as in January 1985 (380 mm in 2 days). When triggered generalized, causing damage to society. Several methods have been proposed to understand the occurrence of these processes in the landscape. The aim of this study is to evaluate the susceptibility to shallow landslides in the Serra do Mar by applying a physically based models, resulting in a stability index, which points in the form of relatively hazard and susceptible areas. The stages of his research were divided in building the Digital Terrain Model in their products derived (angle of slope, curvature, aspect and area of contribution), mapping the scars of 1985 and simulation of susceptibility scenarios. The maps of the parameters topographic, as well as the susceptibility maps were correlated with the scars map and evaluated using the indices of Scars Concentration (SC) and Landslide Potential (LP). 216 scars were mapped into here area of 2.5 km² with an estimated production of 135.525 m³ sediments. The results show that slopes with angles between 30° and 40° with rectilinear curvature were the most susceptible. The area was considered unstable, according to the model in all scenarios, with the best calibration for scenario C2. The use of different methods showed to be satisfactory and consistent when analyzing with these results. Moreover they can assist as tools for decision support in planning the soil use and, especially in regions where much frequent the occurrence of mass movement. Therefore, the result of susceptibility to shallow landslides in the Serra do Mar can help in the mitigation actions and politicaladministrative environment, aiming minimizes the environmental and social impact of future events.

Many past literatures have examined the predictive power of implied volatility versus that of historical volatility, but they have showed divergent conclusions. One of the major differences among these studies is the methods that they used to obtain implied volatility. The VIX index, introduced in 1993, provides a model-free and directly observable source of implied volatility data. The VIX futures is an actively traded VIX derivative product, and its prices are believed to contain market’s expectation about future volatility. By analyzing the relationship between the VIX futures prices and the realized volatilities of the 30-day period that these VIX futures contracts cover, this paper finds that the VIX futures contracts with shorter maturities have predictive power on future realized volatility, but they are upwardly biased estimates. The predictive power, however, decreases as the time to maturity increases. The outstanding VIX futures contracts with the nearest expiration dates outperform GARCH estimates based on historical return data at predicting future realized volatility.

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Abstract The competitiveness of enterprises improves and lies in exploring, analyzing and solving the problem constantly, to keep and build an environment suitable for long term management. This research aims to discuss the profit factors which deeply influence the enterprise, let the case company understand the possible income statement faced in the future after analyzing, and reduce the risk of management by preparations in advance. To the case company, there are two main factors which influence profitability - oil price and the exchange rate of new Taiwan dollar to U.S. dollar. This research makes an analysis, do a prediction and judge to the future price trend. The crude oil has already stepped into high price era, and it is predicted to last for a while. Regardless the main reason that the oil-producing country has tasted the wealth that the high oil price has brought, the demand for crude oil is increasing constantly in China, and the high oil price doesn¡¦t bring great damage to the economy growth and inflation of the global, all sorts of signs show that high oil price will not be discontinued, and the exchange rate of new Taiwan dollar to U.S. dollar depends on the currency policy of U.S. dollar and the development of Taiwan¡¦s economy. And because the ability of consume is still strong constantly in the U.S., the Taiwan¡¦s policy to China has been indistinct as well, all enterprises are full of uncertainty to the future. Those factors influence the economy in Taiwan, thus the long-term pressure of exchange rate appreciation slackens relatively .It is unavoidable to fluctuate in a short time, when the hot money is passed in and out the stock market recently , the fluctuation becomes increasing and violent. But on long terms, in order to maintain the competition advantage for Taiwan exports, the possibility of wide range fluctuation on exchange rate is small. Finally, in order to seek the stable development for case company in the future , they issues new stock to raise the capital in secondary market and invest to the relative enterprises to obtain raw materials , so it is another main purpose of this research to evaluate rational value of new shares for reference.

As the number of college students studying science continues to grow, it is important to identify variables that predict their success. The literature indicates that motivation and learning strategy use facilitate science success. Research findings show these variables can change throughout a semester and differ by performance level, gender and ethnicity. However, significant predictors of performance vary by research study and by group. The current study looks beyond the traditional predictors of grade point averages, SAT scores and completion of advanced placement (AP) chemistry to consider a comprehensive set of variables not previously investigated within the same study. Research questions address the predictive ability of motivation constructs and learning strategies for success in introductory college chemistry, how these variables change throughout a semester, and how they differ by performance level, gender and ethnicity. Participants were 413 introductory college chemistry students at a highly selective university in the southeast. Participants completed the Chemistry Motivation Questionnaire (CMQ) and Learning Strategies section of the Motivated Strategies for Learning Questionnaire (MSLQ) three times during the semester. Self-efficacy, effort regulation, assessment anxiety and previous achievement were significant predictors of chemistry course success. Levels of motivation changed with significant decreases in self-efficacy and increases in personal relevance and assessment anxiety. Learning strategy use changed with significant increases in elaboration, critical thinking, metacognitive self-regulation skills and peer learning, and significant decreases in time and study management and effort regulation. High course performers reported the highest levels of motivation and learning strategy use. Females reported lower intrinsic motivation, personal relevance, self-efficacy and critical thinking, and higher assessment anxiety, rehearsal and organization. Self-efficacy predicted performance for males and females, while self-determination, help-seeking and time and study environment also predicted female success. Few differences in these variables were found between ethnicity groups. Self-efficacy positively predicted performance for Asians and Whites, and metacognitive self-regulation skills negatively predicted success for Other students. The results have implications for college science instructors who are encouraged to collect and utilize data on students’ motivation and learning strategy use, promote both in science classes, and design interventions for specific students who need more support.

In this study, the inability to in a future meet the electricity demand and the urge to change the consumption behavior considered. In a smart grid context there are several possible ways to do this. Means include ways to increase the consumer’s awareness, add energy storages or build smarter homes which can control the appliances. To be able to implement these, indications on how the future consumption will be could be useful. Therefore we look further into how a framework for short-term consumption predictions can be created using electricity consumption data in relation to external factors. To do this a literature study is made to see what kind of methods that are relevant and which qualities is interesting to look at in order to choose a good prediction method. Case Based Reasoning seemed to be able to be suitable method. This method was examined further and built using relational databases. After this the method was tested and evaluated using datasets and evaluation methods CV, MBE and MAPE, which have previously been used in the domain of consumption prediction. The result was compared to the results of the winning methods in the ASHRAE competition. The CBR method was expected to perform better than what it did, and still not as good as the winning methods from the ASHRAE competition. The result showed that the CBR method can be used as a predictor and has potential to make good energy consumption predictions. and there is room for improvement in future studies.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1987. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 201-211).

The lack of training data is a common problem in machine learning. One solution to thisproblem is to use transfer learning to remove or reduce the requirement of training data.Selecting datasets for transfer learning can be difficult however. As a possible solution, thisstudy proposes the domain similarity metrics document vector distance (DVD) and termfrequency-inverse document frequency (TF-IDF) distance. DVD and TF-IDF could aid inselecting datasets for good transfer learning when there is no data from the target domain.The simple metric, shared vocabulary, is used as a baseline to check whether DVD or TF-IDF can indicate a better choice for a fine-tuning dataset. SQuAD is a popular questionanswering dataset which has been proven useful for pre-training models for transfer learn-ing. The results were therefore measured by pre-training a model on the SQuAD datasetand fine-tuning on a selection of different datasets. The proposed metrics were used tomeasure the similarity between the datasets to see whether there was a correlation betweentransfer learning effect and similarity. The results found a clear relation between a smalldistance according to the DVD metric and good transfer learning. This could prove usefulfor a target domain without training data, a model could be trained on a big dataset andfine-tuned on a small dataset that is very similar to the target domain. It was also foundthat even small amount of training data from the target domain can be used to fine-tune amodel pre-trained on another domain of data, achieving better performance compared toonly training on data from the target domain.

abstract: Phishing is a form of online fraud where a spoofed website tries to gain access to user's sensitive information by tricking the user into believing that it is a benign website. There are several solutions to detect phishing attacks such as educating users, using blacklists or extracting phishing characteristics found to exist in phishing attacks. In this thesis, we analyze approaches that extract features from phishing websites and train classification models with extracted feature set to classify phishing websites. We create an exhaustive list of all features used in these approaches and categorize them into 6 broader categories and 33 finer categories. We extract 59 features from the URL, URL redirects, hosting domain (WHOIS and DNS records) and popularity of the website and analyze their robustness in classifying a phishing website. Our emphasis is on determining the predictive performance of robust features. We evaluate the classification accuracy when using the entire feature set and when URL features or site popularity features are excluded from the feature set and show how our approach can be used to effectively predict specific types of phishing attacks such as shortened URLs and randomized URLs. Using both decision table classifiers and neural network classifiers, our results indicate that robust features seem to have enough predictive power to be used in practice. / Dissertation/Thesis / Masters Thesis Computer Science 2017

This project is built on the idea of predicting future human movement in an area. The algorithm’s predictions are based on previous movements in the area which has to be recorded somehow. For this a device with a motion sensor was setup to monitor the movement in a hallway in an office. This data was then used to test and evaluate the prediction algorithm. To give feedback about the movement and how it is changing to the people working in the office the setup device shows sentences on a monitor which describes the movement. The project resulted in a fully working application which measures people walking by, both when and how fast, and predicts future movement. Due to time constraints of the project the device was only up and running for two weeks. This is enough time to get some understanding of how well the prediction algorithm works, but a longer experiment time would have further helped the evaluation. The results showed that the algorithm can predict most of the events during the day, but is bad at predicting sudden spikes or other unusual behavior. / Projektet är baserat på idén att förutse framtida mänsklig rörelse i ett område. För att noggrant kunna förutse framtida rörelse så behöver man kunna mäta tidigare rörelse. För detta så sattes en anordning upp med en rörelse detektor för att mäta rörelsen i en korridor i ett kontor. Data som samlades in användes sedan för att testa och utvärdera förutsägelse algoritmen. För att ge feed-back om rörelsen och hur den ändras till människorna som jobbade i kontoret så visade anordningen meningar på en skärm som beskrev rörelsen. Projektet resulterade i en fullt fungerade applikation som mäter folk som går förbi, både när och hur snabbt, och förutser framtida rörelse. På grund av tids begränsningar i projektet så var anordningen bara uppe och mätte data i två veckor. Detta är tillräckligt mycket tid för att få någon förståelse över hur bra förutsägelse algoritmen fungerar, men en längre experiment tid skulle ha hjälpt utvärderingen. Resultaten visade att algoritmen kan förutse de flesta händelserna under dagen, men är dålig på att förutse plötsliga spikar eller annat ovanligt beteende.