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"The second edition of Understanding Search Engines: Mathematical Modeling and Text Retrieval follows the basic premise of the first edition by discussing many of the key design issues for building search engines and emphasizing the important role that applied mathematics can play in improving information retrieval. The authors discuss important data structures, algorithms, and software as well as user-centered issues such as interfaces, manual indexing, and document preparation. Readers will find that the second edition includes significant changes that bring the text up to date on current information retrieval methods. For example, the authors have added a completely new chapter on link-structure algorithms used in search engines such as Google, and the chapter on user interface has been rewritten to specifically focus on search engine usability. To reflect updates in the literature on information retrieval, the authors have added new recommendations for further reading and expanded the bibliography. In addition, the index has been updated and streamlined to make it more reader friendly."

"Retrieval Practice – an act to recall past knowledge – is one of six method of learning believed to give the best result. This paper tries to seek in which condition it can be done to give maximum result. Looking at past literature and research, we found that text anxiety is a factor that inhibits the retrieval practice and study process. By doing retrieval practice in low pressure and high frequency, we found text anxiety decrease and give better result.

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Retreival Practice – mengingat kembali pengetahuan yang pernah kita pelajari – merupakan salah satu dari enam cara belajar yang dipercaya memberikan hasil terbaik. Laporan ini membahas dalam kondisi apa cara ini dapat terapkan untuk memberikan hasil yang maksimal. Melihat hasil dari literature dan riset terdahulu, kita menemukan bahwa kecemasan ujian menjadi salah satu faktor yang menghambat efektifitas retrieval practice dan pembelajaran. Dengan melakukan retrieval practive dalam kondisi tekanan rendah dan frekuensi tinggi kita mendapati bahwa kecemasan ujian akan turun sehingga memberikan hasil yang lebih baik."

"Machine Learning (ML) algorithms have opened up new possibilitiesfor the acquisition and processing of documents in InformationRetrieval (IR) systems. Indeed, it is now possible to automate severallabor-intensive tasks related to documents such as categorization andentity extraction. Consequently, the application of machine learning techniquesfor various large-scale IR tasks has gathered significant researchinterest in both the ML and IR communities. This tutorial provides areference summary of our research in applying machine learning techniquesto diverse tasks in Digital Libraries (DL). Digital library portalsare specialized IR systems that work on collections of documentsrelated to particular domains. We focus on open-access, scientific digitallibraries such as CiteSeerx, which involve several crawling, ranking,content analysis, and metadata extraction tasks. We elaborate on thechallenges involved in these tasks and highlight how machine learningmethods can successfully address these challenges."