Классификация веб-страниц на основе алгоритмов машинного обучения

Долгое время появлявшиеся в литературе алгоритмы категоризации веб-страниц оставались в тени метода ключевых слов, который работал достаточно эффективно с англо-язычными сайтами. Поэтому возможности применения к этой задаче появившихся недавно алгоритмов классификации были недостаточно хорошо изучены [2,5,3]. Так, например, строковое ядро (String Subsequence Kernel, SSK) получило большее распространение в биоинформатике для классификации протеинов, нежели в веб-программировании для категоризации веб-страниц. Такие новые методы были непопулярны также из-за их несоответствия высоким требованиям к производительности, предъявляемых интернет-системам. Однако, при наличии должной оптимизации такие алгоритмы могут открыть новые возможности для создания простых в разработке категоризаторов, которые будут эффективны даже для языков со сложной морфологией и грамматикой. В данной работе приведён пример такого рода оптимизаций и предложено два классификатора, их реализующих. Результаты, полученные на практических тестах, очевидные возможности масштабирования, заложенные в эти алгоритмы – всё это даёт повод надеяться, что дальнейшее изучение этого вопроса окажется плодотворным.Novel algorithms of web-page classification have been dominated by widely accepted keyword approach for a long time. The keyword approach has proved to be sufficiently effective for English web-pages. Therefore recently published classification algorithms have not been addressed in web-page classification research at an appropriate scale [2,5,3]. For instance, String Subsequence Kernel (SSK) received much larger attention in Bioinformatics for gene and protein classification than in web-programming for web-page categorization. Such novel methods have proved to be unpopular among Internet system providers also because of their high computational requirements. However, with application of certain optimization approaches, such algorithms can bring development of classification systems to a new level, where high efficiency can be achieved even for languages with complex morphology and grammar. This work represents an example of such optimization attempt and it provides two different realizations for such classifiers. Positive characteristics of presented results and scaling properties of these algorithms encourage further research in this area

Data mining technology for the evaluation of web-based teaching and learning systems

Instructional design for Web-based teaching and learning environments causes problems for two reasons. Firstly, virtual forms of teaching and learning result in little or no direct contact between instructor and learner, making the evaluation of course effectiveness difficult. Secondly, the Web as a relatively new teaching and learning medium still requires more research into learning processes with this technology. We propose data mining – techniques to discover and extract knowledge from a database – as a tool to support the analysis of student learning processes and the evaluation of the effectiveness and usability of Web-based courses. We present and illustrate different data mining techniques for the evaluation of Web-based teaching and learning systems

Browsing a digital library: A new approach for the New Zealand digital library

Browsing is part of the information seeking process, used when information needs are ill-defined or unspecific. Browsing and searching are often interleaved during information seeking to accommodate changing awareness of information needs. Digital Libraries often support full-text search, but are not so helpful in supporting browsing. Described here is a novel browsing system created for the Greenstone software used by the New Zealand Digital Library that supports users in a more natural approach to the information seeking process. © Springer-Verlag Berlin Heidelberg 2003

I Know Why You Went to the Clinic: Risks and Realization of HTTPS Traffic Analysis

Revelations of large scale electronic surveillance and data mining by governments and corporations have fueled increased adoption of HTTPS. We present a traffic analysis attack against over 6000 webpages spanning the HTTPS deployments of 10 widely used, industry-leading websites in areas such as healthcare, finance, legal services and streaming video. Our attack identifies individual pages in the same website with 89% accuracy, exposing personal details including medical conditions, financial and legal affairs and sexual orientation. We examine evaluation methodology and reveal accuracy variations as large as 18% caused by assumptions affecting caching and cookies. We present a novel defense reducing attack accuracy to 27% with a 9% traffic increase, and demonstrate significantly increased effectiveness of prior defenses in our evaluation context, inclusive of enabled caching, user-specific cookies and pages within the same website