In the recent years, we have witnessed a dramatic increment in the volume of spam email. Other related forms of spam are increasingly revealing as a problem of importance, specially the spam on Instant Messaging services (the so called SPIM), and Short Message Service (SMS) or mobile spam.Like email spam, the SMS spam problem can be approached with legal, economic or technical measures. Among the wide range of technical measures, Bayesian filters are playing a key role in stopping email spam. In this paper, we analyze to what extent Bayesian filtering techniques used to block email spam, can be applied to the problem of detecting and stopping mobile spam. In particular, we have built two SMS spam test collections of significant size, in English and Spanish. We have tested on them a number of messages representation techniques and Machine Learning algorithms, in terms of effectiveness. Our results demonstrate that Bayesian filtering techniques can be effectively transferred from email to SMS spam.

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We consider the problem of content-based spam filtering for short text messages that arise in three contexts: mobile (SMS) communication, blog comments, and email summary information such as might be displayed by a lowbandwidth client. Short messages often consist of only a few words, and therefore present a challenge to traditional bag-of-words based spam filters. Using three corpora of short messages and message fields derived from real SMS, blog, and spam messages, we evaluate feature-based and compression-model-based spam filters. We observe that bagof-words filters can be improved substantially using different features, while compression-model filters perform quite well as-is. We conclude that content filtering for short messages is surprisingly effective.

Many head‐and‐neck cancer (HNC) patients treated with radiotherapy suffer significant anatomical changes due to tumor shrinkage or weight loss. The purpose of this study was to assess dose changes over target volumes and organs at risk during intensity‐modulated radiotherapy for HNC patients. Sixteen HNC IMRT patients, all requiring bilateral neck irradiation, were enrolled in the study. A CTplan was performed and the initial dose distribution was calculated. During the treatment, two subsequent CTs at the 15th (CT15) and 25th (CT25) fractions were acquired. The initial plan was calculated on the CT15 and CT25, and dose‐volume differences related to the CTplan were assessed. For target volumes, mean values of nearmaximun absorbed dose (normalD2%) increased at the 25th fraction, and doses covering 95% and 98% of volume decreased significantly at the 15th fraction. Contralateral and ipsilateral parotid gland mean doses increased by 6.1% (range: ‐5.4, 23.5%) and 4.7% (range: ‐9.1, 22.3%), respectively, at CT25. The normalD2% in the spinal cord increased by 1.8 Gy at CT15. Mean absorbed dose increases at CT15 and CT25 were observed in: the lips, 3.8% and 5.3%; the oral cavity, 3.5% and 2.5%; and lower middle neck structure, 1.9% and 1.6%. Anatomical changes during treatment of HNC patients affect dose distribution and induce a loss of dose coverage to target volumes and an overdosage to critical structures. Appropriate organs at risk have to be contoured and monitored in order to know if the initial plan remains suitable during the course of the treatment. Reported dosimetric data can help to identify patients who could benefit from adaptive radiotherapy.PACS numbers: 87.53.Kn, 87.55.Dk