In this talk, we will present the design and evaluation of PeerDB, a peer-to-peer (P2P) distributed data sharing system. PeerDB distinguishes itself from existing P2P systems in several ways. First, it is a full-fledge data management system that supports fine-grain content-based searching. Second, it facilitates sharing of data without shared schema. Third, it combines the power of mobile agents into P2P systems to perform operations at peers' sites. Fourth, PeerDB network is self-configurable, i.e., a node can dynamically optimize the set of peers that it can communicate directly with based on some optimization criterion. / Singapore-MIT Alliance (SMA)

Since the 1970s, music librarians have been discussing the challenges of cataloging music media. In the 1990s, they began work on a Music Thesaurus to provide a multi-faceted approach to indexing, cataloging, and retrieving music media. In 1999 Indiana University proposed a digital music library, to allow for better indexing and retrieval in addition to content-based music retrieval. In 2000, a commercial venture, The Music Genome Project ©, began cataloging sound recordings of popular music by hundreds of musical characteristics and has created a user interface that allows listeners to enter the title and artist of a certain piece of music and receive recommendations for similar music to then purchase via Pandora.com. The following paper will address the question: how might current analyzing and classifying methods be used to provide additional indexing that facilitates retrieval and use of sound recordings by special populations, specifically professionals treating children with Asperger’s syndrome?

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The aim of this essay is to show how to use William Golding's novel Lord of the Flies in the ESL classroom to teach democratic values. Such values include: respect, empathy and the right to free speech. According to Reader-Response theory, the reader brings expectations and knowledge about the subject matter (in this case democracy and its values) to the texts, which influence his/her interpretation. I have applied two different styles of analyzing a text: a Content-Based Approach and Simpson's Communication Triangle. The Content-Based Approach, in accordance with Reader-Response Theory, builds on students' knowledge and previous experience and focuses on the content to be acquired. The Simpson's Communication Triangle, on the other hand, connects reading, discussing and writing. Both of the approaches are designed to enhance the students' reading responses by providing different forums for sharing, such as discussions and writing (diary entries) from one of the character's perspective. The idea of creating Reader-Response journals is supported by multiple forms of theoretical study, and the assignments explained in this essay have been designed upon this research.

Accompanying with the rapid growth of Internet, people around the world can easily distribute, browse, and share as much information as possible through the Internet. The enormous amount of information, however, causes the information overload problem that is beyond users¡¦ limited information processing ability. Therefore, recommender systems arise to help users to look for useful information when they cannot describe the requirements precisely. The filtering techniques in recommender systems can be divided into content-based filtering (CBF) and collaborative filtering (CF). Although CF is shown to be superior over CBF in literature, personalized document recommendation relies more on CBF simply because of its text content in nature. Nevertheless, document recommendation task provides a good chance to integrate both techniques into a hybrid one, and enhance the overall recommendation performance. The objective of this research is thus to propose a hybrid filtering approach for personalized document recommendation. Particularly, latent Dirichlet allocation to uncover latent semantic structure in documents is incorporated to help us to either obtain robust document similarity in CF, or explore user profiles in CBF. Two experiments are conducted accordingly. The results show that our proposed approach outperforms other counterparts on the recommendation performance, which justifies the feasibility of our proposed approach in real applications.

Literature digital library is one of the most important resources to preserve civilized asset. To provide more effective and efficient information search, many systems are equipped with a browsing interface that aims to ease the article searching task. A browsing interface is associated with a subject directory, which guides the users to identify articles that need their information need. A subject directory contains a set (or a hierarchy) of subject categories, each containing a number of similar articles. How to group articles in a literature digital library is the theme of this thesis. Previous work used either document classification or document clustering approaches to dispatching articles into a set of article clusters based on their content. We observed that articles that meet a single user¡¦s information need may not necessarily fall in a single cluster. In this thesis, we propose to make use of both Web log and article content is clustering articles. We proposed two hybrid approaches, namely document categorization based method and document clustering based method. These alternatives were compared to other content-based methods. It has been found that the document categorization based method effectively reduces the number of required click-through at the expense of slight increase of entropy that measures the content heterogeneity of each generated cluster.

Recommender systems are information retrieval tools helping users in their information seeking tasks and guiding them in a large space of possible options. Many hybrid recommender systems are proposed so far to overcome shortcomings born of pure content-based (PCB) and pure collaborative filtering (PCF) systems. Most studies on recommender systems aim to improve the accuracy and efficiency of predictions. In this thesis, we propose an online hybrid recommender strategy (CBCFdfc) based on content boosted collaborative filtering algorithm which aims to improve the prediction accuracy and efficiency. CBCFdfc combines content-based and collaborative characteristics to solve problems like sparsity, new item and over-specialization. CBCFdfc uses fuzzy clustering to keep a certain level of prediction accuracy while decreasing online prediction time. We compare CBCFdfc with PCB and PCF according to prediction accuracy metrics, and with CBCFonl (online CBCF without clustering) according to online recommendation time. Test results showed that CBCFdfc performs better than other approaches in most cases. We, also, evaluate the effect of user-specified parameters to the prediction accuracy and efficiency. According to test results, we determine optimal values for these parameters. In addition to experiments made on simulated data, we also perform a user study and evaluate opinions of users about recommended movies. The results that are obtained in user evaluation are satisfactory. As a result, the proposed system can be regarded as an accurate and efficient hybrid online movie recommender.

The information that World Wide Web (WWW) provides have grown up very rapidly in recent years, which resulted in new approaches for people to reach the information they need. Although web pages and search engines are indeed strong enough for us to reach what we want, it is not an efficient solution to present data and wait people to reach it. Some more creative and beneficial methods had to be developed for decreasing the time to reach the information and increase the quality of the information. Recommendation systems are one of the ways for achieving this purpose. The idea is to design a system that understands the information user wants to obtain from user actions, and to find the information similar to that. Several studies have been done in this field in order to develop a recommendation system which is capable of recommending movies, books, web sites and similar items like that. All of them are based on two main principles, which are collaborative filtering and content based recommendations. Within this thesis work, a recommendation system approach which combines both content based (CB) and collaborative filtering (CF) approaches by capturing the effect of time like purchase time or release time. In addition to this temporal behavior, the influence of demographic information of user on purchasing habits is also examined this system which is called &ldquo / TDRS&rdquo / .

The objective of the present work is to employ a multiple-instance learning image retrieval system by incorporating a spatial similarity measure. Multiple-Instance learning is a way of modeling ambiguity in supervised learning given multiple examples. From a small collection of positive and negative example images, semantically relevant concepts can be derived automatically and employed to retrieve images from an image database. The degree of similarity between two spatial relations is linked to the distance between the associated nodes in an Interval Neighbor Group (ING). The shorter the distance, the higher degree of similarity, while a longer one, a lower degree of similarity. Once all the pairwise similarity values are derived, an ensemble similarity measure will then integrate these pairwise similarity assessments and give an overall similarity value between two images. Therefore, images in a database can be quantitatively ranked according to the degree of ensemble similarity with the query image. Similarity retrieval method evaluates the ensemble similarity based on the spatial relations and common objects present in the maximum common subimage between the query and a database image are considered. Therefore, reliable spatial relation features extracted from the image, combined with a multiple-instance learning paradigm to derive relevant concepts, can produce desirable retrieval results that better match user¡¦s expectation. In order to demonstrate the feasibility of the proposed approach, two sets of test for querying an image database are performed, namely, the proposed RSS-ING scheme v.s. 2D Be-string similarity method, and single-instance vs. multiple-instance learning. The performance in terms of similarity curves, execution time and memory space requirement show favorably for the proposed multiple-instance spatial similarity-based approach.

AR is hypothesized to comprise above-threshold academic language proficiency, personal characteristics, topical knowledge, academic skills, and academic auxiliaries (motivation, study skills, engagement, work drive, emotional stability, affective schemata, and metacognitive strategies).The participants were 36 international adults, studying pre-university academic English at intensive institutes in Florida who volunteered to take the CLEAR during the summer of 2004. Data were collected via the CLEAR multiple-choice knowledge test and essay test, teacher ratings, examinee feedback, and external measures.Results showed the CLEAR knowledge test functions well at the item level although overall scores are only moderately consistent. The essay scoring consistency was satisfactory, perhaps partly due to the purpose-built scoring tool Good support for content-related validity claims was found for the dynamic simulation overall, for the stimulus materials, for the knowledge test items, for the essay prompt, and for the essay scoring tool. The concurrent measure of teacher ratings correlated with the knowledge test, but not with the content-based essay. Concerning construct-related claims of validity, support was evinced through the literature review as well as through inter-subtest correlation. External measures suggested some discriminant evidentiary support. Examinees perceived that the CLEAR closely resembled the target environment, they judged the CLEAR quality to be a key feature, and they would recommend the CLEAR to a friend for the growth experience.