In this paper, we describe the influence of physical proximity on the development of collaborative relationships between scientific researchers and on the execution of their work. Our evidence is drawn from our own studies of scientific collaborators, as well as from observations of research and development activities collected by other investigators. These descriptions provide the foundation for a discussion of the actual and potential role of communications technology in professional work, especially for collaborations carried out at a distance.

The 117 manuscripts submitted for the Hypertext'91 conference were assigned to members of the review committee, using a variety of automated methods based on information retrieval principles and Latent Semantic Indexing. Fifteen reviewers provided exhaustive ratings for the submitted abstracts, indicating how well each abstract matched their interests. The automated methods do a fairly good job of assigning relevant papers for review, but they are still somewhat poorer than assignments made manually by human experts and substantially poorer than an assignment perfectly matching the reviewers' own ranking of the papers. A new automated assignment method called "n of 2n" achieves better performance than human experts by sending reviewers more papers than they actually have to review and then allowing them to choose part of their review load themselves. Keywords: Conferences, Program Committees, Reviewers, Referees, Manuscripts, Papers, Assignment, Matching, Interests, Latent Semantic Ind...

In this paper, we examine the problem of automatic image captioning. Given a training set of captioned images, we want to discover correlations between image features and keywords, so that we can automatically find good keywords for a new image. We experiment thoroughly with multiple design alternatives on large datasets of various content styles, and our proposed methods achieve up to a 45% relative improvement on captioning accuracy over the state of the art.

The paper presents a taxonomy that summarises and highlights the major research into Latent Semantic Analysis (LSA) based educational applications. The taxonomy identifies five main research themes and emphasises the point that even after more than 15 years of research, much is left to be discovered to bring the LSA theory to maturity. The paper provides a framework for LSA researchers to publish their results in a format that is comprehensive, relatively compact, and useful to other researchers.

In this paper, we examine the problem of automatic image captioning. Given a training set of captioned images, we want to discover correlations between image features and keywords, so that we can automatically find good keywords for a new image. We experiment thoroughly with multiple design alternatives on large datasets of various content styles, and our proposed methods achieve up to a 45% relative improvement on captioning accuracy over the state of the art.