With the help of the simple and world-wide accepted technique of tagging users can help to collaboratively provide metadata over previously uncharted collections of multimedia documents. However, the semantics of tags are rather limited and not always as helpful in disclosing a dataset like a proper ontology can be. In this paper we look at how applying both syntactic and semantic techniques to connect tags to ontologies can help to quickly get more semantics about a tag. Then we describe how the user can help to improve the described techniques and we sketch how user collaboration helps the content provider in obtaining professional quality metadata, and enables personalization for the user. Finally, we also consider how these semantically richer tags can help us in navigating, searching and representation of the datasets.

Abstract. We consider the problem of how to group information when multiple similarities are known. For a group of people, we may know their education, geographic location and family connections and want to cluster the people by treating all three of these similarities simultaneously. Our approach is to store each similarity as a slice in a tensor. The similarity measures are generated by comparing features. Generally, the object similarity matrix is dense. However it can be stored implicitly as the product of a sparse matrix, representing the object-feature matrix, and its transpose. For this new type of tensor where dense slices are stored implicitly, we have created a new decomposition called Implicit Slice Canonical Decomposition (IMSCAND). Our decomposition is equivalent to the tensor CANDECOMP/PARAFAC decomposition, which is a higher-order analogue of the matrix Singular Value decomposition (SVD) and Principal Component Analysis (PCA). From IMSCAND we obtain compilation feature vectors which are clustered using k-means. We demonstrate the applicability of IMSCAND on a set of journal articles with multiple similarities.

This paper presents a first attempt at using pedigree-based data to improve record linkage. It describes a composite metric for similarity and a mechanism to extract relevant generational features. Results on a large data set demonstrate promise. 1

Abstract — In the context of a possibilistic framework for detection of object co-reference, evaluators have been defined as operators that compare two values and express the belief that such values are co-referent. Hereby, co-reference of two values means that these values describe the same entity in the real world. In this paper, a class of evaluators is investigated that determines the belief of co-reference based on semantical connections between values of the universe. These semantical connections are modeled by means of binary relations. In case these binary relations are not a-priori given, they can be (partially) learned in an iterative co-reference detection schema.

Joel W. Cohen, Dr. Lap-Ming Wun and Ms. Janet Greenblatt for their careful review of the

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Voter registration databases maintain lists of registered voters that are used to determine who is and is not eligible to vote in an election. As such, accurate voter registration databases form a cornerstone of the electoral process. In the United States, each state maintains its own voter registration database. It is not uncommon for a voter to become registered in two states, for example as a result of moving from one state to the other or of living in one state and working in one another. In this paper, we report on a pilot interstate voter registration database matching project between the two states of Oregon and Washington whose goal was to explore the feasibility of using database matching to identify voters registered in the two states, and to do so with as much openness and transparency as possible. We describe the matching algorithms used, the procedures taken with found matches, and the resulting actions taken on actual voter registrations. We also discuss several directions for improving matching algorithms and procedures.

Voter registration databases maintain lists of registered voters that are used to determine who is and is not eligible to vote in an election. As such, accurate voter registration databases form a cornerstone of the electoral process. In the United States, each state maintains its own voter registration database. It is not uncommon for a voter to become registered in two states, for example as a result of moving from one state to the other or of living in one state and working in one another. In this paper, we report on a pilot interstate voter registration database matching project between the two states of Oregon and Washington whose goal was to explore the feasibility of using database matching to identify voters registered in the two states, and to do so with as much openness and transparency as possible. We describe the matching algorithms used, the procedures taken with found matches, and the resulting actions taken on actual voter registrations. We also discuss several directions for improving matching algorithms and procedures.

Though security is nothing new, the way that security has become a part of our daily life is unprecedented. Attacks, misuse or unauthorized access of information is of great concern today which makes the protection of documents through digital media is a priority problem. This urges users to devise new data hiding techniques through steganography principle to protect and secure the data of vital significance. Considerable amount of work has been carried out by different researchers on steganography. In this work the authors propose a novel audio based steganographic method for wav and mp3 format for hiding information. The proposed approach works by selecting the embedding positions using some mathematical function and maps each four bit of the secret message in each of the selected positions in a specified manner. A pseudo random number generator is used here to locate the embedding positions of the message bits randomly. This solution is independent of the nature of the data to be hidden and produces a stego audio with minimum degradation.