A video-based real-time vehicle detection method by classified background learning

A new two-level real-time vehicle detection method is proposed in order to meet the robustness and efficiency requirements of real world applications. At the high level, pixels of the background image are classified into three categories according to the characteristics of Red, Green, Blue (RGB) curves. The robustness of the classification is further enhanced by using
line detection and pattern connectivity. At the lower level, an exponential forgetting algorithm with adaptive parameters for different categories is utilised to calculate the background and reduce the distortion by the small motion of video cameras. Scene tests show that the proposed method is more robust and faster than previous methods, which is very suitable for real-time vehicle detection in outdoor environments, especially concerning locations where the level of illumination changes frequently and speed detection is important.

Thats classified: class politics and adolescence in twin peaks

Twin Peaks arguably paved the way for the television programmes currently popular with adolescent audiences, like The OC and Veronica Mars and, in it, many of the issues and representational strategies in those later programmes have their earlier manifestation. Specifically, the Twin Peaks plotline evinces a set of cultural anxieties about class-difference. Twin Peaks creates a cultural microcosm of American society that is paradoxically writ large by the limited parameters of an isolated community. Within a constricted space, characters are depicted as both individuals and as archetypes of a class location.

Low-SES students wrongly classified, but who cares?

Equity experts agree with research findings that the metrics for measuring socioeconomic status (SES) are problematic. But they disagree that it really matters.

How accurately can convertibles be classified as debt or equity for tax purposes? Evidence from Australia

The New Business Tax System (Debt and Equity) Act established a set ofcriteria by which convertible securities could be classified as “debt-like” or “equity-like” for tax purposes. Using data on 256 convertible issues made in Australia between 2001 and 2012, we show that there is a strong relation between, on the one hand, a convertible’s ex ante classification determined at issuance using the tax criteria and, on the other hand, its ex post classification based on the conversion premium at maturity. We conclude that the criteria have been an efficient means of classifying convertibles. We also find an industry effect where debt-like convertibles are more likely to be associated with the resources, metals and mining firms, whilst equity-like are mainly issued by the finance sector. This finding is consistent with the solution to a finance-sequencing problem in the former case, and the impact of capital adequacy regulation in the latter.

Enabling fine-grained multi-keyword search supporting classified sub-dictionaries over encrypted cloud data

Using cloud computing, individuals can store their data on remote servers and allow data access to public users through the cloud servers. As the outsourced data are likely to contain sensitive privacy information, they are typically encrypted before uploaded to the cloud. This, however, significantly limits the usability of outsourced data due to the difficulty of searching over the encrypted data. In this paper, we address this issue by developing the fine-grained multi-keyword search schemes over encrypted cloud data. Our original contributions are three-fold. First, we introduce the relevance scores and preference factors upon keywords which enable the precise keyword search and personalized user experience. Second, we develop a practical and very efficient multi-keyword search scheme. The proposed scheme can support complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords. Third, we further employ the classified sub-dictionaries technique to achieve better efficiency on index building, trapdoor generating and query. Lastly, we analyze the security of the proposed schemes in terms of confidentiality of documents, privacy protection of index and trapdoor, and unlinkability of trapdoor. Through extensive experiments using the real-world dataset, we validate the performance of the proposed schemes. Both the security analysis and experimental results demonstrate that the proposed schemes can achieve the same security level comparing to the existing ones and better performance in terms of functionality, query complexity and efficiency.