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In this fast developing world, the number of motor vehicles is increasing rapidly but road resources remain limited, causing severe congestion problem of city traffic. In order to predict short-term traffic condition accurately, we propose a short-term traffic condition prediction method for urban road network based on improved support vector machine. As outliers inevitably exist in collected traffic...
Bilinear convolutional neural networks (BCNN) model, the state-of-the-art in fine-grained image recognition, fails in distinguishing the categories with subtle visual differences. We design a novel BCNN model guided by user click data (C-BCNN) to improve the performance via capturing both the visual and semantical content in images. Specially, to deal with the heavy noise in large-scale click data,...
We study the problem of zero-shot classification in which we don't have labeled data in target domain. Existing approaches learn a model from source domain and apply it without adaptation to target domain, which is prone to domain shift problem. To solve the problem, we propose a novel Learning Discriminative Instance Attribute(LDIA) method. Specifically, we learn a projection matrix for both the...
Data-driven facial animation has attracted much attention in recent years. Existing facial animation methods may not preserve the topology structure, and cannot achieve a natural face. This paper proposes a new data-driven facial animation method based on hypergraph learning. It drives a neutral face to a certain expression face. This paper assumes that neutral face has similar topology with the expression...
In order to overcome the limitation of existing data cleansing methods working on massive data, in this paper, we propose a generic semantic-based framework using parallelized processing model for effective big data cleansing. We also use an improved Semantic-Based Keyword Matching Algorithm to deal with duplicate data. Experimental results show that this parallelized framework with improved Semantic-Based...
Demonstration of the equipment was limited to a single weapon performance and efficiency in the past. It is more and more inclined to carry out the demonstration research from the system of systems. Through analyzing the problem of the weapon system of systems design, we puts forward the design ideas of weapon system of systems and give the overall framework of software framework that can support...
Although citizen science projects can engage a very large number of volunteers to collect volumes of data, they are susceptible to issues with data quality. Our experience with eBird, which is a broad-scale citizen science project to collect bird observations, has shown that a massive effort by volunteer experts is needed to screen data, identify outliers and flag them in the database. The increasing...
Citizen scientists, who are volunteers from the community that participate as field assistants in scientific studies, enable research to be performed at much larger spatial and temporal scales than trained scientists can cover. Species distribution modeling, which involves understanding species-habitat relationships, is a research area that can benefit greatly from citizen science. The eBird project...
Formal methods are introduced into system design process that use rigorously specified mathematical models to build target systems. It can establish a precise and unambiguous model of a complex system. This paper takes an example for a complex NP-complete problem such as a class scheduling problem. By using the specification language Z, it designs and describes a formal mathematical model of a class...
Support vector regression (SVR) is now a well-established method for non-stationary series forecasting, because of its good generalization ability and guaranteeing global minima. However, only using SVR hardly get satisfied accuracy for complicated frequency spectrum prediction in frequency monitor system (FMS) of high frequency radar. Empirical mode decomposition (EMD) is perfectly suitable for nonlinear...
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