784309
Learning subjective relevance to facilitate information access
As the amount of available electronic information is dramatically increasing, the ability for rapid and e ective access to information has become critical. Most traditional information access methods rely on measures of relevance based on information content. We propose a new approach which augments existing information access methods with subjective relevance learned from user feedback. We developed an adaptive system which helps users access information by employing learned knowledge about which documents are likely to be relevant, given the current user's information need and user pro le. This system is based on a model, called a relevance network, which learns and generalizes relevance information in a rapid, cost-e ective, and incremental manner. We present the design of the relevance network and results of experimental evaluation.
29860220
A Bookmarking Service for Organizing and Sharing URLs
Web browser bookmarking facilities predominate as the method of choice for managing URLs. In this paper, we describe some deficiencies of current bookmarking schemes, and examine an alternative to current approaches. We present WebTagger , an implemented prototype of a personal bookmarking service that provides both individuals and groups with a customizable means of organizing and accessing Web-based information resources. In addition, the service enables users to supply feedback on the utility of these resources relative to their information needs, and provides dynamically-updated ranking of resources based on incremental user feedback. Individuals may access the service from anywhere on the Internet, and require no special software. This service greatly simplifies the process of sharing URLs within groups, in comparison with manual methods involving email. The underlying bookmark organization scheme is more natural and flexible than current hierarchical schemes supported by the major Web browsers, and enables rapid access to stored bookmarks.
7673579
Managing personal and group collections of information
The internet revolution has dramatically increased the amount of information available to users. Various tools such as search engines have been developed to help users find the information they need from this vast repository. Users often also need tools to help manipulate the growing amount of useful information they have discovered. Current tools available for this purpose are typically local components of web browsers designed to manage URL bookmarks. They provide limited functionalities to handle high information complexities.
28375225
User-centered indexing for adaptive information access
We are focusing on information access tasks characterized by large volume of hypermedia connected technical documents, a need for rapid and effective access to familiar information, and long-term interaction with evolving information. The problem for technical users is to build and maintain a personalized task-oriented model of the information to quickly access relevant information. We propose a solution which provides user-centered adaptive information retrieval and navigation. This solution supports users in customizing information access over time. It is complementary to information discovery methods which provide access to new information, since it lets users customize future access to previously found information. It relies on a technique, called Adaptive Relevance Network, which creates and maintains a complex indexing structure to represent personal user's information access maps organized by concepts. This technique is integrated within the Adaptive HyperMan system, which helps NASA Space Shuttle flight controllers organize and access large amount of information. It allows users to select and mark any part of a document as interesting, and to index that part with user-defined concepts. Users can then do subsequent retrieval of marked portions of documents. This functionality allows users to define and access personal collections of information, which are dynamically computed. The system also supports collaborative review by letting users share group access maps. The adaptive relevance network provides long-term adaptation based both on usage and on explicit user input. The indexing structure is dynamic and evolves over time. Learning and generalization support flexible retrieval of information under similar concepts. The network is geared towards more recent information access, and automatically manages its size in order to maintain rapid access when scaling up to large hypermedia space. We present results of simulated learning experiments.
20277412
Adaptive hypertext navigation based on user goals and context
Hypertext systems allow flexible access to topics of information, but this flexibility has disadvantages. Users often become lost or overwhelmed by choices. An adaptive hypertext system can overcome these disadvantages by recommending information to users based on their specific information needs and preferences. Simple associative matrices provide an effective way of capturing these user preferences. Because the matrices are easily updated, they support the kind of dynamic learning required in an adaptive system.HYPERFLEX, a prototype of an adaptive hypertext system that learns, is described. Informal studies with HYPERFLEX clarify the circumstances under which adaptive systems are likely to be useful, and suggest that HYPERFLEX can reduce time spent searching for information by up to 40%. Moreover, these benefits can be obtained with relatively little effort on the part of hypertext authors or users.The simple models underlying HYPERFLEX's performance may offer a general and useful alternative to more sophisticated modelling techniques. Conditions under which these models, and similar adaptation techniques, might be most useful are discussed.
12562104
Collaborative information agents on the World Wide Web
In this paper, we present DIAMS, a system of distributed, collaborative information agents which help users access, collect, organize and exchange information on the World Wide Web. Personal agents provide their owners dynamic displays of well organized information collections, as well as friendly information management utilities. Personal agents exchange information with one another. They also work with other types of information agents such as matchmakers and knowledge experts to facilitate collaboration and communication.
16407407
An interactive method for fuzzy multiple attribute group decision making
In this paper, we develop an interactive method for multiple attribute group decision making under fuzzy environment. The method can be used in situations where the information about attribute weights is partly known, the weights of decision makers are expressed in exact numerical values or triangular fuzzy numbers, and the attribute values are triangular fuzzy numbers. The method transforms fuzzy decision matrices into their expected decision matrices, constructs the corresponding normalized expected decision matrices by two simple formulas, and then aggregates these normalized expected decision matrices into a complex decision matrix. Moreover, the decision makers are asked to provide their preferences gradually in the course of interactions. By solving linear programming models, the method diminishes the given alternative set gradually, and finally finds the most preferred alternative. By using the method, the decision makers can provide and modify their preference information gradually in the process of decision making so as to make the decision result more reasonable. The method can not only reflect the importance of the given arguments and the ordered positions of the arguments, but also relieve the influence of unfair arguments on the decision result. Finally, a practical problem is used to illustrate the developed method.
12533129
Procrastination for leakage-aware rate-monotonic scheduling on a dynamic voltage scaling processor
As the dynamic voltage scaling (DVS) technique provides system engineers the flexibility to trade-off the performance and the energy consumption, DVS has been adopted in many computing systems. However, the longer a job executes, the more energy in the leakage current the device/processor consumes for the job. To reduce the energy consumption resulting from the leakage current, a system might enter the dormant mode. This paper targets energy-efficient rate-monotonic scheduling for periodic real-time tasks on a uniprocessor DVS system with non-negligible leakage power consumption. An on-line simulated scheduling strategy and a virtually blocking time strategy are developed for procrastination scheduling to reduce energy consumption. The proposed algorithms derive a feasible schedule for real-time tasks with worst-case guarantees for any input instance. Experimental results show that our proposed algorithms could derive energy-efficient solutions.
43053494
Heaps with Bits
We show how to improve the complexity of heap operations and heapsort using extra bits. We first study the parallel complexity of implementing priority queue operations on a heap. The tradeoff betw ...
2587881
Isomap Based on the Image Euclidean Distance
Scientists find that the human perception is based on the similarity on the manifold of data set. Isometric feature mapping (isomap) is one of the representative techniques of manifold. It is intuitive, well understood and produces reasonable mapping results. However, if the input data for manifold learning are corrupted with noises, the isomap algorithm is topologically unstable. In this paper, we present an improved manifold learning method when the input data are images - the image Euclidean distance based isomap (imisomap), in which we use a new distance for images called image Euclidean distance (IMED). Experimental results demonstrate a consistent performance improvement of the algorithm imisomap over the traditional isomap based on Euclidean distance
38658502
Learning by Discovering Problem Solving Heuristics Through Experience
The authors present a system, called SAFE (strategy acquisition from experience), which incorporates novel methods for discovering domain-dependent problem-solving heuristics. SAFE is implemented as a sorting system whose sorting strategies are represented as production rules. SAFE initially uses the insertion sort strategy to solve problems. After solving each given problem. SAFE learns symbolic rules from the solution path which is obtained by applying the existing heuristic information. By one or several processes of learning. SAFE is able to obtain the heuristics to sort new problems with minimum exchanges of elements. The notion of shortcut, an effective inductive learning bias for reducing the hypothesis space to be searched during learning, is introduced. >