ECONOMISTS FREQUENTLY BASE THEIR REASONING on ratios between significant variables.' Accountants often construct key financial ratios from such items as quick assets, liabilities, inventories, sales, and earnings. If measurements for firms in diverse size groups are reduced to a common order of magnitude, these ratios may be of use in international comparisons and historical growth comparisons. For purposes of theory construction, however, our standard must be high, and stablity, or plainly systematic variation, in ratios must be found in order to enhance their usefulness. The present study is a modest contribution to the construction of a theory of financial structure. It seeks to analyze the financial ratios with respect to the three exogenous variables-industry, size, and growth.Research dealing with the use of financial ratios for studying the corporate structure seems to fall into two broad categories. These authors have sought to study the changes in the structures of American enterprises during World War I, World War II, and in the intervening period. The second line of inquiry has been to examine the corporate fiinancial structure as of a given date, in the nature of synchronic studies. The only work of this type is that of Walter A. Chudson.6 Chudson ran arrays of ratios by industry, size, and profitability, but he did not attempt to offer theoretical justifications or economic explanations for his findings. His study was published by the * The author wishes to acknowledge with gratitude the many helpful suggestions and critical comments of J. Fred Weston.
Email spam is a much studied topic, but even though current email spam detecting software has been gaining a competitive edge against text based email spam, new advances in spam generation have posed a new challenge: image-based spam. Image based spam is email which includes embedded images containing the spam messages, but in binary format. In this paper, we study the characteristics of image spam to propose two solutions for detecting image-based spam, while drawing a comparison with the existing techniques. The first solution, which uses the visual features for classification, offers an accuracy of about 98%, i.e. an improvement of at least 6% compared to existing solutions. SVMs (Support Vector Machines) are used to train classifiers using judiciously decided color, texture and shape features. The second solution offers a novel approach for near duplication detection in images. It involves clustering of image GMMs (Gaussian Mixture Models) based on the Agglomerative Information Bottleneck (AIB) principle, using Jensen-Shannon divergence (JS) as the distance measure.
This study investigates the risk of insider threats associated with different applications within a financial institution. Extending routine activity theory (RAT) from criminology literature to information systems security, hypotheses regarding how application characteristics, namely value, inertia, visibility, accessibility, and guardians, cause applications to be exposed to insider threats are developed. Routine activity theory is synthesized with survival modeling, specifically a Weibull hazard model, and users' system access behaviors are investigated using seven months of field data from the institution. The inter-arrival times of two successive unauthorized access attempts on an application are employed as the measurement of risk. For a robustness check, the daily number of unauthorized attempts experienced by an application as an alternative measurement of risk are introduced and a zero-inflated Poisson Gamma model is developed. The Markov chain Monte Carlo (MCMC) method is used for model estimations. The results of the study support the empirical application of routine activity theory in understanding insider threats, and provide a picture of how different applications have different levels of exposure to such threats. Theoretical and practical implications for risk management regarding insider threats are discussed. This study is among the first that uses behavioral logs to investigate victimization risk and attack proneness associated with information assets.
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