16016667
Finding more useful information faster from web search results
In this paper, we propose a prototype system for automatic generation of concept hierarchies to be used as an overview of search results. The system sends a user's query to five search engines and receives a returned list of relevant web pages. The system then extracts query-oriented concept terms from snippets that come with the returned hits. Concept terms are organized into a concept hierarchy using a co-occurrence-based classification technique. Finally, concepts in returned documents are dynamically highlighted according to terms in the selected concept branch that lead to the chosen document. The user study shows that concept hierarchies do provide easy navigation and browsing of web returned documents. The results also show that users can find a document of interest no matter how low it is ranked in the retrieved list.
3764367
eLearning assessment through textual analysis of class discussions
Most of the elearning classes use discussion forums to support class interaction. This paper presents a novel approach that automatically assesses student class performance by analyzing knowledge density in online class messages. Class performance of students is evaluated from three perspectives: the quality of their course work, the quantity of their efforts, and the activeness of their participation; three measures - knowledge density, message length, and message count, are derived from class messages respectively. Together, they are used to calculate a performance indicator score. The experiment shows that there is a high correlation between the performance indicator scores and the actual grades assigned by instructors. The rank orders of students by the performance indicator score are highly correlated with those by the actual grades as well.
3822190
Improving Concept Hierarchy Development for Web Returned Documents Using Automatic Classification
3223686
Personalized Knowledge Discovery: Mining Novel Association Rules from Text
This paper presents a methodology for personalized knowledge discovery from text. It derives a user’s background knowledge from his/her background documents, and exploits such knowledge to evaluate the novelty of discovered knowledge in the form of association rules by measuring the semantic distance between the antecedent and the consequent of a rule in the background knowledge. The experiment results show that the proposed user-oriented novelty measure is highly correlated with the human subjective rule novelty and usefulness ratings. It outperforms seven major objective interestingness measures and the WordNet novelty measure for identifying novel and useful rules.
3822993
Learning by Examples: Identifying Key Concepts from Text Using Pre-Defined Inputs
62599943
Finding nuggets in documents: A machine learning approach
Document keyphrases provide a concise summary of a document's content, offering semantic metadata summarizing a document. They can be used in many applications related to knowledge management and text mining, such as automatic text summarization, development of search engines, document clustering, document classification, thesaurus construction, and browsing interfaces. Because only a small portion of documents have keyphrases assigned by authors, and it is time-consuming and costly to manually assign keyphrases to documents, it is necessary to develop an algorithm to automatically generate keyphrases for documents. This paper describes a Keyphrase Identification Program (KIP), which extracts document keyphrases by using prior positive samples of human identified phrases to assign weights to the candidate keyphrases. The logic of our algorithm is: The more keywords a candidate keyphrase contains and the more significant these keywords are, the more likely this candidate phrase is a keyphrase. KIP's learning function can enrich the glossary database by automatically adding new identified keyphrases to the database. KIP's personalization feature will let the user build a glossary database specifically suitable for the area of hissher interest. The evaluation results show that KIP's performance is better than the systems we compared to and that the learning function is effective. © 2006 Wiley Periodicals, Inc.
38430290
3-NASH is PPAD-Complete
In this paper, we improve a recent result of Daskalakis, Goldberg and Papadimitriou on PPAD-completeness of 4-Nash, showing that 3-Nash is PPAD-complete.
671264
Patterns of Multimodal Input Usage in Non-Visual Information Navigation
Multimodal input is known to be advantageous for graphical user interfaces, but its benefits for non-visual interaction are unknown. To explore this issue, an exploratory study was conducted with fourteen sighted subjects on a system that allows speech input and hand input on a touchpad. Findings include: (1) Users chose between these two input modalities based on the types of operations undertaken. Navigation operations were done primarily with touchpad input, while non-navigation instructions were carried out primarily using speech input. (2) Multimodal error correction was not prevalent. Repeating a failed operation until it succeeded and trying other methods in the same input modality were dominant error-correction strategies. (3) The modality learned first was not necessarily the primary modality used later, but a training order effect existed. These empirical results provide guidelines for designing non-visual multimodal input and create a comparison baseline for a subsequent study with blind users.
641995
An Admission Control Scheme for Predictable Server Response Time for Web Accesses
The diversity in web object types and their resource requirements contributes to the unpredictability of web service provisioning. In this paper, an eÆcient admission control algorithm, PACERS, is proposed to provide di erent levels of services based on the server workload characteristics. Service quality is ensured by periodical allocation of system resources based on the estimation of request rate and service requirements of prioritized tasks. Admission of lower priority tasks is restricted during high load periods to prevent denial-of-services to high priority tasks. A doublequeue structure is implemented to reduce the e ects of estimation inaccuracy and to utilize the spare capacity of the server, thus increasing the system throughput. Response delays of the high priority tasks are bounded by the length of the prediction period. Theoretical analysis and experimental study show that the PACERS algorithm provides desirable throughput and bounded response delay to the prioritized tasks, without any signi cant impact on the aggregate throughput of the system under various workload.
10227949
Analysis of nonsmooth vector-valued functions associated with second-order cones
Abstract.Let be the Lorentz/second-order cone in . For any function f from to , one can define a corresponding function fsoc(x) on by applying f to the spectral values of the spectral decomposition of x∈ with respect to . We show that this vector-valued function inherits from f the properties of continuity, (local) Lipschitz continuity, directional differentiability, Fréchet differentiability, continuous differentiability, as well as (ρ-order) semismoothness. These results are useful for designing and analyzing smoothing methods and nonsmooth methods for solving second-order cone programs and complementarity problems.
35009889
Tree Domination in Graphs