1251407
Reference metadata extraction using a hierarchical knowledge representation framework
The integration of bibliographical information on scholarly publications available on the Internet is an important task in the academic community. Accurate reference metadata extraction from such publications is essential for the integration of metadata from heterogeneous reference sources. In this paper, we propose a hierarchical template-based reference metadata extraction method for scholarly publications. We adopt a hierarchical knowledge representation framework called INFOMAP, which automatically extracts metadata. The experimental results show that, by using INFOMAP, we can extract author, title, journal, volume, number (issue), year, and page information from different kinds of reference styles with a high degree of precision. The overall average accuracy is 92.39% for the six major reference styles compared in this study.
6185076
Text categorization using automatically acquired domain ontology
In this paper, we describe ontology-based text categorization in which the domain ontologies are automatically acquired through morphological rules and statistical methods. The ontology-based approach is a promising way for general information retrieval applications such as knowledge management or knowledge discovery. As a way to evaluate the quality of domain ontologies, we test our method through several experiments. Automatically acquired domain ontologies, with or without manual editing, have been used for text categorization. The results are quite satisfactory. Furthermore, we have developed an automatic method to evaluate the quality of our domain ontology.
4516498
Using Instant Messaging to Provide an Intelligent Learning Environment
Instant Messaging enables learners and educators to interact in an on-line environment. In this paper, we propose an intelligent ChatBot system, based on instant messaging, for student on-line coaching in an English learning environment. The proposed ChatBot facilitates synchronous communication with students by using ready reference materials including, dictionaries, authorized conversation material with speaking, and a question-answering function. The agent records and analyzes conversations so that the teacher can assess students' progress. Our contribution in this paper is that we integrate the NLP Tool and AIML into an instant messaging-based ChatBot for English as a Second Language programs.
6036700
Functional Implementation Techniques for CPU Cache Memories
As the performance gap between processors and main memory continues to widen, increasingly aggressive implementations of cache memories are needed to bridge the gap. In this paper, we consider some of the issues that are involved in the implementation of highly optimized cache memories and survey the techniques that can be used to help achieve the increasingly stringent design targets and constraints of modern processors. In particular, we consider techniques that enable the cache to be accessed quickly and still achieve a good hit ratio. We also consider issues such as area cost and bandwidth requirements. Trace-driven simulations of a TPC-C-like workload and selected applications from the SPEC95 benchmark suite are used in the paper to compare the performance of some of the techniques.
38774895
Fast Algorithms for the Dominating Set Problem on Permutation Graphs
AnO(n log logn) (resp.O(n2 log2n)) algorithm is presented to solve the minimum cardinality (resp. weight) dominating set problem on permutation graphs, assuming the input is a permutation. The best-known previous algorithm was given by FArber and Keil, where they use dynamic programming to get anO(n2 (resp.O(n3)) algorithm. Our improvement is based on the following three factors: (1) an observation on the order among the intermediate terms in the dynamic programming, (2) a new construction formula for the intermediate terms, and (3) efficient data structures for manipulating these terms.
6232755
Algorithms for minimum covering by cliques and maximum clique in claw-free perfect graphs
An algorithm is given for determining a minimum cardinality clique cover ongraphs that do not contain claws, odd holes or odd anti-holes. These graphs are also shown to be perfect so that we obtain a new proof of the strong perfect graph theorem for claw-free graphs. A by-product of our analysis is an algorithm for determining a maximum cardinality clique on claw-free perfect graphs.
11584462
The automatic improvement of locality in storage systems
Disk I/O is increasingly the performance bottleneck in computer systems despite rapidly increasing disk data transfer rates. In this article, we propose Automatic Locality-Improving Storage (ALIS), an introspective storage system that automatically reorganizes selected disk blocks based on the dynamic reference stream to increase effective storage performance. ALIS is based on the observations that sequential data fetch is far more efficient than random access, that improving seek distances produces only marginal performance improvements, and that the increasingly powerful processors and large memories in storage systems have ample capacity to reorganize the data layout and redirect the accesses so as to take advantage of rapid sequential data transfer. Using trace-driven simulation with a large set of real workloads, we demonstrate that ALIS considerably outperforms prior techniques, improving the average read performance by up to 50% for server workloads and by about 15% for personal computer workloads. We also show that the performance improvement persists as disk technology evolves. Since disk performance in practice is increasing by only about 8% per year, the benefit of ALIS may correspond to as much as several years of technological progress.
269822
Visual Cue Cluster Construction via Information Bottleneck Principle and Kernel Density Estimation
Recent research in video analysis has shown a promising direction, in which mid-level features (e.g., people, anchor, indoor) are abstracted from low-level features (e.g., color, texture, motion, etc.) and used for discriminative classification of semantic labels. However, in most systems, such mid-level features are selected manually. In this paper, we propose an information-theoretic framework, visual cue cluster construction (VC3), to automatically discover adequate mid-level features. The problem is posed as mutual information maximization, through which optimal cue clusters are discovered to preserve the highest information about the semantic labels. We extend the Information Bottleneck framework to high-dimensional continuous features and further propose a projection method to map each video into probabilistic memberships over all the cue clusters. The biggest advantage of the proposed approach is to remove the dependence on the manual process in choosing the mid-level features and the huge labor cost involved in annotating the training corpus for training the detector of each mid-level feature. The proposed VC3 framework is general and effective, leading to exciting potential in solving other problems of semantic video analysis. When tested in news video story segmentation, the proposed approach achieves promising performance gain over representations derived from conventional clustering techniques and even the mid-level features selected manually.