16533233
Improving text classification using local latent semantic indexing
Latent semantic indexing (LSI) has been shown to be extremely useful in information retrieval, but it is not an optimal representation for text classification. It always drops the text classification performance when being applied to the whole training set (global LSI) because this completely unsupervised method ignores class discrimination while only concentrating on representation. Some local LSI methods have been proposed to improve the classification by utilizing class discrimination information. However, their performance improvements over original term vectors are still very limited. In this paper, we propose a new local LSI method called "local relevancy weighted LSI" to improve text classification by performing a separate single value decomposition (SVD) on the transformed local region of each class. Experimental results show that our method is much better than global LSI and traditional local LSI methods on classification within a much smaller LSI dimension.
459986
A New Statistical Approach To Chinese Pinyin Input
Chinese input is one of the key challenges for Chinese PC users. This paper proposes a statistical approach to Pinyin-based Chinese input. This approach uses a trigram-based language model and a statistically based segmentation. Also, to deal with real input, it also includes a typing model which enables spelling correction in sentence-based Pinyin input, and a spelling model for English which enables modeless Pinyin input.
432736
ReCoM: reinforcement clustering of multi-type interrelated data objects
Most existing clustering algorithms cluster highly related data objects such as Web pages and Web users separately. The interrelation among different types of data objects is either not considered, or represented by a static feature space and treated in the same ways as other attributes of the objects. In this paper, we propose a novel clustering approach for clustering multi-type interrelated data objects, ReCoM (Reinforcement Clustering of Multi-type Interrelated data objects). Under this approach, relationships among data objects are used to improve the cluster quality of interrelated data objects through an iterative reinforcement clustering process. At the same time, the link structure derived from relationships of the interrelated data objects is used to differentiate the importance of objects and the learned importance is also used in the clustering process to further improve the clustering results. Experimental results show that the proposed approach not only effectively overcomes the problem of data sparseness caused by the high dimensional relationship space but also significantly improves the clustering accuracy.
14828051
MRSSA: an iterative algorithm for similarity spreading over interrelated objects
We introduce the Multiple Relationship Similarity Spreading Algorithm (MRSSA) to enhance IR effectiveness. This method has similarity computed in an iterative "spreading" fashion for multiple object types, combining both inter- and intra-object relationships. We demonstrate the value of this approach in the context of the WWW, where the key objects are web pages and queries, Relationships considered are derived from hyperlinks (in- and out-links) and click-through logs.
6802371
Improving web search results using affinity graph
In this paper, we propose a novel ranking scheme named Affinity Ranking (AR) to re-rank search results by optimizing two metrics: (1) diversity -- which indicates the variance of topics in a group of documents; (2) information richness -- which measures the coverage of a single document to its topic. Both of the two metrics are calculated from a directed link graph named Affinity Graph (AG). AG models the structure of a group of documents based on the asymmetric content similarities between each pair of documents. Experimental results in Yahoo! Directory, ODP Data, and Newsgroup data demonstrate that our proposed ranking algorithm significantly improves the search performance. Specifically, the algorithm achieves 31% improvement in diversity and 12% improvement in information richness relatively within the top 10 search results.
17711119
IRC: an iterative reinforcement categorization algorithm for interrelated Web objects
Most existing categorization algorithms deal with homogeneous Web data objects, and consider interrelated objects as additional features when taking the interrelationships with other types of objects into account. However, focusing on any single aspects of these interrelationships and objects does not fully reveal their true categories. In this paper, we propose a categorization algorithm, the iterative reinforcement categorization algorithm (IRC), to exploit the full interrelationships between the heterogeneous objects on the Web. IRC attempts to classify the interrelated Web objects by iterative reinforcement between individual classification results of different types via the interrelationships. Experiments on a clickthrough log dataset from MSN search engine show that, with the Fl measures, IRC achieves a 26.4% improvement over a pure content-based classification method, a 21% improvement over a query metadata-based method, and a 16.4% improvement over a virtual document-based method. Furthermore, our experiments show that IRC converges rapidly.
2042582
Pose determination of a cylinder using reprojection transformation
Extracting useful and robust feature points from a cylinder image for 3D pose determination is a relatively difficult task, since there are no feature points or lines on the cylinder. In this study, a key vector from a cylinder image is found to yield a transformation. The cylinder is then ''reprojected'' from the original image to obtain a new image using the transformation. The new image reveals some important features capable of determining the 3D cylinder position robustly. The proposed method can yield an exact and analytical solution without the requirement of using any special marks, extra equipment or assumptions. Additionally, a problem in which both circles on the cylinder are not completely visible due to the viewing angle can be resolved by this method. Finally, experimental results are provided to verify the theory and show the robustness of our method.
7399730
An Almost Linear Time 2.8334-Approximation Algorithm for the Disc Covering Problem
The disc covering problem asks to cover a set of points on the plane with a minimum number of fix-sized discs. We develop an O(n(logn)2(loglogn)2) deterministic time 2.8334-approximation algorithm for this problem. Previous approximation algorithms [7,3,6], when used to achieve the same approximation ratio for the disc covering problem, will have much higher time complexity than our algorithms.
3449616
Fast and memory-efficient regular expression matching for deep packet inspection
Packet content scanning at high speed has become extremely important due to its applications in network security, network monitoring, HTTP load balancing, etc. In content scanning, the packet payload is compared against a set of patterns specified as regular expressions. In this paper, we first show that memory requirements using traditional methods are prohibitively high for many patterns used in packet scanning applications. We then propose regular expression rewrite techniques that can effectively reduce memory usage. Further, we develop a grouping scheme that can strategically compile a set of regular expressions into several engines, resulting in remarkable improvement of regular expression matching speed without much increase in memory usage. We implement a new DFA-based packet scanner using the above techniques. Our experimental results using real-world traffic and patterns show that our implementation achieves a factor of 12 to 42 performance improvement over a commonly used DFA- based scanner. Compared to the state-of-art NFA-based implementation, our DFA-based packet scanner achieves 50 to 700 times speedup.
23589102
Bounds of the longest directed cycle length for minimal strong digraphs
Abstract In this paper we present the upper and lower bounds of the longest directed cycle length for minimal strong digraphs in terms of the numbers of vertices and arcs. These bounds are both sharp. In addition, we give analogous results for minimal 2-edge connected graphs.
9224231
The plain old television in a smart apartment
Our GO (www.go-outside.org) project focuses on community access for the cognitively impaired population. In addition to devising new facilities, we are interested in using existing appliances found in an apartment to interact with a user to help with a specific task, e.g., making a trip to the doctor. This requires establishment of collaboration protocols between the appliances (and smart agents) and user to be established. These protocols must enforce certain safety properties, e.g., that the user get timely information to start a task. We will show how we use a light-weight formal modeling toolkit to reason about a protocol's ability to meet safety properties