6591350
Inferring Ongoing Activities of Workstation Users by Clustering Email
We are interested in automatically discovering the key ongoing activities of a workstation user, such as committees to which she belongs, writing projects in which she is involved, etc., based on the contents of her workstation. The thesis underlying our research is that this collection of user activities can be automatically inferred from the variety of data available on most users’ workstations, including their emails, files, online calendar, and history of web page accesses. Knowledge about the user's activities could be used in a variety of ways, such as cross-indexing email, calendar events, files, and web accesses according to activity, or automatically producing a 'briefing folder' for each meeting on the user's calendar. We describe here our initial research on inferring such activities by examining only the user's email. In particular, we describe a variety of unsupervised clustering methods designed for clustering emails by user activity, and the use of information extractors and pretrained classifiers to infer additional information about each discovered cluster. Experimental results are presented for emails from three users.
3154384
Text clustering with extended user feedback
Text clustering is most commonly treated as a fully automated task without user feedback. However, a variety of researchers have explored mixed-initiative clustering methods which allow a user to interact with and advise the clustering algorithm. This mixed-initiative approach is especially attractive for text clustering tasks where the user is trying to organize a corpus of documents into clusters for some particular purpose (e.g., clustering their email into folders that reflect various activities in which they are involved). This paper introduces a new approach to mixed-initiative clustering that handles several natural types of user feedback. We first introduce a new probabilistic generative model for text clustering (the SpeClustering model) and show that it outperforms the commonly used mixture of multinomials clustering model, even when used in fully autonomous mode with no user input. We then describe how to incorporate four distinct types of user feedback into the clustering algorithm, and provide experimental evidence showing substantial improvements in text clustering when this user feedback is incorporated.
1605324
Using contextual information to improve retrieval performance
In this paper, we propose a contextual retrieval framework which incorporates the user and global contextual information into the probabilistic retrieval model. We investigate different techniques of using contextual information to improve information retrieval performance in details. In particular, (1) we use the related text contextual information for query expansion; (2) we use the granularity information to construct the document level index and paragraph level index; (3) we use the geographic information for filtering. In addition, a new term weighting function BM5O is proposed based on the global context information. This framework is adaptable and extensible. If there is a new context category, we can extend the existing search system to accommodate it. Finally, we report our experimental findings on TREC data sets.
26991946
Publish/Subscribe in a Mobile Environment
A publish/subscribe system dynamically routes and delivers events from sources to interested users, and is an extremely useful communication service when it is not clear in advance who needs what information. In this paper we discuss how a publish/subscribe system can be extended to operate in a mobile environment, where events can be generated by moving sensors or users, and subscribers can request delivery at handheld and/or mobile devices. We describe how the publish/subscribe system itself can be distributed across multiple (possibly mobile) computers to distribute load, and how the system can be replicated to cope with failures, message loss, and disconnections.
7153278
Maximum genus and girth of graphs
Abstract In this paper, a lower bound on the maximum genus of a graph in terms of its girth is established as follows: let G be a simple graph with minimum degree at least three, and let g be the girth of G. Then γ(G)⩾ g−2 2(g−1) β(G)+ 1 g−1 except for G=K 4 , where β(G) denotes the cycle rank of G and K4 is the complete graph with four vertices.
18171732
Data Placement in Intermittently Available Environments
In this paper, we address the problem of data placement in a grid based multimedia environment, where the resource providers, i.e. servers, are intermittently available. The goal is to optimize the system performance by admitting maximum number of users into the system while ensuring user Quality of Service (QoS). We define and formulate various placement strategies that determine the degree of replication necessary for video objects by using a cost-based optimization procedure based on predictions of expected requests under various time-map scenarios and QoS demands. We also devise methods for dereplication of videos based on changes in popularity and server usage patterns. Our performance results indicate the benefits obtained the judicious use of dynamic placement strategies.
41865724
Improved Image Coding with Classified VQ and Side-Match VQ
A new scheme for vector quantization (VQ) is proposed in this paper. We employ side-match and classified criteria for designing VQ codebooks to combat blocking effects induced from high compression rates, and then we use the proposed algorithm to improve the reconstructed image quality. Simulation results demonstrate the better image quality to compare with that produced from conventional schemes objectively and subjectively, at the cost of reasonable encoding complexity.