In the following we show that any convex set that contains a congruent copy of any set of diameter one (universal cover) has area at least 0.832. This considerably improves the lower bound for Lebesgue's universal cover problem, using a combination of computer search and geometric bounds.
The goal of the ongoing project described in this paper is evaluation of the utility of Latent Semantic Analysis (LSA) for unsupervised word sense discrimination. The hypothesis is that LSA can be used to compute context vectors for ambiguous words that can be clustered together-with each cluster corresponding to a different sense of the word. In this paper we report first experimental result on tightness, separation and purity of sense-based clusters as a function of vector space dimensionality and using different distance metrics.
Internet scam is fraudulent or intentionally misleading information posted on the web, usually with the intent of tricking people into sending money or disclosing sensitive information. We describe an application of logistic regression to detection of Internet scam. The developed system automatically collects 43 characteristic statistics of the websites from 11 online sources and computes the probability that a given website is malicious. We present an empirical evaluation of this system, which shows that its precision and recall are 98%.
We describe a crowdsourcing system, called SmartNotes, which detects security threats related to web browsing, such as Internet scams, deceptive sales of substandard products, and websites with intentionally misleading information. It combines automatically collected data about websites with user votes and comments, and uses them to identify potential threats. We have implemented it as a browser extension, which is available for free public use.
Email is a private medium of communication, and the inherent privacy constraints form a major obstacle in developing effective spam filtering methods which require access to a large amount of email data belonging to multiple users. To mitigate this problem, we envision a privacy preserving spam filtering system, where the server is able to train and evaluate a logistic regression based spam classifier on the combined email data of all users without being able to observe any emails using primitives such as homomorphic encryption and randomization. We analyze the protocols for correctness and security, and perform experiments of a prototype system on a large scale spam filtering task.State of the art spam filters often use character n-grams as features which result in large sparse data representation, which is not feasible to be used directly with our training and evaluation protocols. We explore various data independent dimensionality reduction which decrease the running time of the protocol making it feasible to use in practice while achieving high accuracy.
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