By Bhavani Thuraisingham, Murat Kantarcioglu, Latifur Khan (auth.), Hsinchun Chen, Christopher C. Yang, Michael Chau, Shu-Hsing Li (eds.)
This booklet constitutes the refereed lawsuits of the Pacific Asia Workshop on Intelligence and safety Informatics, PAISI 2009, held in Bangkok, Thailand, in April 2009.
The 10 revised complete papers, 7 revised brief papers including 1 keynote lecture have been rigorously reviewed and chosen from a variety of submissions. The papers are equipped in topical sections on terrorism informatics and crime research company probability administration emergency reaction and surveillance info entry and defense, in addition to information and textual content mining.
Read or Download Intelligence and Security Informatics: Pacific Asia Workshop, PAISI 2009, Bangkok, Thailand, April 27, 2009. Proceedings PDF
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Extra resources for Intelligence and Security Informatics: Pacific Asia Workshop, PAISI 2009, Bangkok, Thailand, April 27, 2009. Proceedings
Possibly that was because the larger number of authors could disruptors effectively differentiating authorship. 6 Conclusion A Cybercrime forensic method for Chinese web information authorship analysis was proposed in this paper. Based on authors’ writing style, linguistic features, and structural features were extracted. The support vector machine algorithm was adopted as learning algorithm. To test the effect of the method, blog, BBS, and e-mail dataset was tested. The experimental results showed that the accuracy of linguistic and structural features combination was better than that of features separately.
Under the pressure of capital, they borrowed short-term loans to meet operating demand so financial structure became worse. The vicious circle motivated evil top management to misappropriate corporate money for keeping stock price rising. In the meantime, fraudulent financial reporting could conceal the embezzlement to make investors and banks trust them. Moreover, frequent schemes included overstating revenues through fictitious sales, obtaining money in nominal accounts such as temporary payment or prepayment for purchases, recording loans from related party into accounts receivable, falsified some accounts like accounts receivable etc.
The result can be shown as Table 5. Table 5. F. 000 Exploring Fraudulent Financial Reporting with GHSOM 37 We made use of F-value to determine the significance of each independent variable. The result of corresponding p-value indicates that eight variables have statistically significant effects, including ROA, CR, QR, DR, CFR, CFAR, SPR and ZScore. e. discriminant loadings) to compare the discriminant power of individual variable. In brief, it is used to estimate the relative importance of each variable to the discriminant function based on the absolute value of structure coefficient.