BD+30 3639: The Infrared Spectrum During Post-AGB Stellar Evolution

We present a radiative-transfer calculation which reproduces the infrared spectrum of the planetary nebula BD~+30$^{\circ}$3639. We calculate the transfer process through absorption and scattering in a spherical-symmetric multi-grain dust shell. The emission of transiently heated particles is taken into account, as well as polycyclic aromatic hydrocarbons. We obtain an acceptable fit to most of the spectrum, including the PAH infrared bands. At submillimetre wavelengths the observed emission is larger than the model predicts, indicating that large dust conglomerates (``f{}luffy grains'') may be needed as an additional constituent. The fit favours a distance of $\ge 2 \,$kpc, which implies that BD~+30$^\circ$3639 has evolved from a massive progenitor of several solar masses. A low dust-to-gas mass ratio is found in the ionised region. The calculations yield an original mass-loss rate of 2\times10^{-4} \msolar \peryr on the Asymptotic Giant Branch. Using this mass-loss rate, we calculate how the infrared spectrum has evolved during the post-AGB evolution. We show in particular the evolution of the IRAS colours during the preceding post-AGB evolution.Comment: accepted for publication in MNRAS. LaTeX, 15 pages, hardcopy and 8 figures available from [email protected] or [email protected]

Detecting social cliques for automated privacy control in online social networks

As a result of the increasing popularity of online social networking sites, millions of people spend a considerable portion of their social life on the Internet. The information exchanged in this context has obvious privacy risks. Interestingly, concerns of social network users about these risks are related not only to adversarial activities but also to users they are directly connected to (friends). In particular, many users want to occasionally hide portions of their information from certain groups of their friends. To satisfy their users' needs, social networking sites have introduced privacy mechanisms (such as Facebook's friend lists) that enable users to expose a particular piece of their information only to a subset of their friends. Unfortunately, friend lists need to be specified manually. As a result, users frequently do not use these mechanisms, either due to a lack of concern about privacy, but more often due to the large amount of time required for the necessary setup and management. In this paper, we propose a privacy control approach that addresses this problem by automatically detecting social cliques among the friends of a user. In our context, a social clique is a group of people whose members share a significant level of social connections, possibly due to common interests (hobbies) or a common location. To find cliques, we present an algorithm that, given a small number of friends (seed), uses the structure of the social graph to generate an approximate clique that contains this seed. The cliques found by the algorithm can be transformed directly into friend lists, making sure that a piece of sensitive data is exposed only to the members of a particular clique. Our evaluation on the Facebook platform shows that our method delivers good results, and the cliques that our algorithm identifies typically cover a large fraction of the actual social cliques. © 2012 IEEE

POISED: Spotting Twitter Spam Off the Beaten Paths

Cybercriminals have found in online social networks a propitious medium to spread spam and malicious content. Existing techniques for detecting spam include predicting the trustworthiness of accounts and analyzing the content of these messages. However, advanced attackers can still successfully evade these defenses. Online social networks bring people who have personal connections or share common interests to form communities. In this paper, we first show that users within a networked community share some topics of interest. Moreover, content shared on these social network tend to propagate according to the interests of people. Dissemination paths may emerge where some communities post similar messages, based on the interests of those communities. Spam and other malicious content, on the other hand, follow different spreading patterns. In this paper, we follow this insight and present POISED, a system that leverages the differences in propagation between benign and malicious messages on social networks to identify spam and other unwanted content. We test our system on a dataset of 1.3M tweets collected from 64K users, and we show that our approach is effective in detecting malicious messages, reaching 91% precision and 93% recall. We also show that POISED's detection is more comprehensive than previous systems, by comparing it to three state-of-the-art spam detection systems that have been proposed by the research community in the past. POISED significantly outperforms each of these systems. Moreover, through simulations, we show how POISED is effective in the early detection of spam messages and how it is resilient against two well-known adversarial machine learning attacks