How to determine the truthfulness of a piece of information becomes an increasingly urgent need for users. In this paper, we propose a method called MFSV, to determine the truthfulness of fact statements. We first cal...
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Untruthful information spreads on the web, which may mislead users or have a negative impact on user experience. In this paper, we propose a method called Multi-verifier to determine the truthfulness of a fact stateme...
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vegetation continuous The scale-location specific control on distribution was investigated through wavelet transforms approaches in subtropical mountain-hill region, Fujian, China. The Normalized Difference Vegetatio...
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vegetation continuous The scale-location specific control on distribution was investigated through wavelet transforms approaches in subtropical mountain-hill region, Fujian, China. The Normalized Difference Vegetation Index （NDVI） was calculated as an indicator of vegetation greenness using Chinese Environmental Disaster Reduction Satellite images along latitudinal and longitudinal transects. Four scales of variations were identified from the local wavelet spectrum of NDVI, with much stronger wavelet variances observed at larger scales. The characteristic scale of vegetation distribution within mountainous and hilly regions in Southeast China was around 20 km. Significantly strong wavelet coherency was generally examined in regions with very diverse topography, typically characterized as small mountains and hills fractured by rivers and residents. The continuous wavelet based approaches provided valuable insight on the hierarchical structure and its corresponding characteristic scales of ecosystems, which might be applied in defining proper levels in multilevel models and optimal bandwidths in Geographically Weighted Regression.
Modern educational theories, such as collaborative learning, constructivism and inquiry learning, have achieved many successes in real-world applications. Especially, with the development of information technologies, ...
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ISBN:
(纸本)9780889869431
Modern educational theories, such as collaborative learning, constructivism and inquiry learning, have achieved many successes in real-world applications. Especially, with the development of information technologies, there have been several online collaborative learning platforms in practice. Unfortunately, as these platforms are either too complicated or too expensive, none of them are suitable for us in the practice of STEM+. Moreover, most of these platforms are in English, while we are using Chinese as our teaching language. Using an online collaborative learning platform (OCLP) named Zask, this paper reported our practice in online collaborative learning on course Introduction to database System. According to the data collected from the first round of our practice, it shows that users' active participations in Zask could benefit for both teaching and learning, and then provide positive effects in education.
Although there have been many efforts for management of uncertain data, evaluating probabilistic inference queries, a known NP-hard problem, is still a big challenge, especially for querying data with highly correlati...
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Online shopping has been accepted by more and more consumers. C2C websites provide thousands of offers for consumers as a mainstream e-commerce platform. When customers search products in C2C website, some returned of...
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Although pseudo relevant feedback is an effective query expansion method, query drift away from the topic has been occurred frequently. Therefore, the first important problem is how to identify relevant documents in t...
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This paper addresses a new text classification method: Sparse Topic Model, which represents documents by the sparse coding of topics. Topics contain more semantic information than words, so it's more effective for...
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ISBN:
(纸本)9781479902590
This paper addresses a new text classification method: Sparse Topic Model, which represents documents by the sparse coding of topics. Topics contain more semantic information than words, so it's more effective for feature representation of documents. Topics are extracted from documents by LDA in an unsupervised way. Based on these topics, sparse coding is applied to discover more high-level representation. We compare the Sparse Topic Model with the traditional methods, such as SVM, and the experimental result show that the proposed method achieves better performance, especially when the number of training examples is limited. The effect of topic number and word number per topic on the performance is also investigated. Due to the unsupervised characteristic of Sparse Topic Model, it's very useful for real application.
Wireless Sensor Networks (WSNs) can be viewed as a new type of distributed databases. data management technology is one of the core technologies of WSNs. In this demo we show a Query Processing system based on TinyOS ...
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