28386762
Taxonomy Building and Machine Learning Based Automatic Classification for Knowledge-Oriented Chinese Questions
In this paper, we propose a taxonomy for knowledge-oriented question, and study the machine learning based classification for knowledge-oriented Chinese questions. By knowledge-oriented questions, we mean questions carrying information or knowledge about something, which cannot be well described by previous taxonomies. We build the taxonomy after the study of previous work and analysis of 6776 Chinese knowledge-oriented questions collected from different realistic sources. Then we investigate the new task of knowledge-oriented Chinese questions classification based on this taxonomy. In our approach, the popular SVM learning method is employed as classification algorithm. We explore different features and their combinations and different kernel functions for the classification, and use different performance metrics for evaluation. The results demonstrate that the proposed approach is desirable and robust. Thorough error analysis is also conduced.
14292213
Title extraction from bodies of HTML documents and its application to web page retrieval
This paper is concerned with automatic extraction of titles from the bodies of HTML documents. Titles of HTML documents should be correctly defined in the title fields; however, in reality HTML titles are often bogus. It is desirable to conduct automatic extraction of titles from the bodies of HTML documents. This is an issue which does not seem to have been investigated previously. In this paper, we take a supervised machine learning approach to address the problem. We propose a specification on HTML titles. We utilize format information such as font size, position, and font weight as features in title extraction. Our method significantly outperforms the baseline method of using the lines in largest font size as title (20.9%-32.6% improvement in F1 score). As application, we consider web page retrieval. We use the TREC Web Track data for evaluation. We propose a new method for HTML documents retrieval using extracted titles. Experimental results indicate that the use of both extracted titles and title fields is almost always better than the use of title fields alone; the use of extracted titles is particularly helpful in the task of named page finding (23.1% -29.0% improvements).
7625528
The performance impact of traffic patterns on routing protocols in mobile ad hoc networks
In this paper, we examine the communication model widely used in simulation studies of mobile ad hoc networks (MANETs). We find that the communication model uses an overly simplistic traffic pattern which restricts the number of connections that originate from each source node to be one or two, and thus the communication model may not represent traffic patterns in many potential applications in MANETs. We then propose a new communication model which extends the previous communication model to include a more general traffic pattern that varies the number of connections per source node. We study the performance impact of traffic patterns on various routing protocols via detailed simulations of a MANET of 112 mobile nodes. Our simulation results show that many of the conclusions drawn in previous protocol comparison studies no longer hold under the new communication model. These results motivate the need for performance evaluation of MANETs to not only include rich and diverse mobility models as has been done in the past but also include diverse traffic patterns that stress a wide set of protocol design issues.
10720283
Discriminant Analysis on Embedded Manifold
Previous manifold learning algorithms mainly focus on uncovering the low dimensional geometry structure from a set of samples that lie on or nearly on a manifold in an unsupervised manner. However, the representations from unsupervised learning are not always optimal in discriminating capability. In this paper, a novel algorithm is introduced to conduct discriminant analysis in term of the embedded manifold structure. We propose a novel clustering algorithm, called Intra-Cluster Balanced K-Means (ICBKM), which ensures that there are balanced samples for the classes in a cluster; and the local discriminative features for all clusters are simultaneously calculated by following the global Fisher criterion. Compared to the traditional linear/kernel discriminant analysis algorithms, ours has the following characteristics: 1) it is approximately a locally linear yet globally nonlinear discriminant analyzer; 2) it can be considered a special Kernel-DA with geometry-adaptive-kernel, in contrast to traditional KDA whose kernel is independent to the samples; and 3) its computation and memory cost are reduced a great deal compared to traditional KDA, especially for the cases with large number of samples. It does not need to store the original samples for computing the low dimensional representation for new data. The evaluation on toy problem shows that it is effective in deriving discriminative representations for the problem with nonlinear classification hyperplane. When applied to the face recognition problem, it is shown that, compared with LDA and traditional KDA on YALE and PIE databases, the proposed algorithm significantly outperforms LDA and
14398394
Optimizing routing in structured peer-to-peer overlay networks using routing table redundancy
Structured peer-to-peer (p2p) overlay networks like CAN, Chord, Pastry and Tapestry [3, 6, 5, 9] provide a self-organizing substrate for large-scale peer-to-peer applications. These systems provide efficient, fault-tolerant routing, object location and load balancing within a self-organization overlay network. In this paper, we show how redundant information that is collected as part of the normal overlay maintenance protocol can be exploited to improve the performance of routing, in terms of both the number of routing hops and routing delay penalty. We use Pastry as a concrete example to describe the set of optimizations and to evaluate their improvement in routing performance via a large scale simulation using a realistic network topology model. We then discuss how these optimizations can be applied to other structured p2p overlays.
39349635
OpenMP for Networks of SMPs
In this paper, we present the first system that implements OpenMP on a network of shared-memory multiprocessors. This system enables the programmer to rely on a single, standard, shared-memory API for parallelization within a multiprocessor and between multiprocessors. It is implemented via a translator that converts OpenMP directives to appropriate calls to a modified version of the TreadMarks software distributed shared-memory (SDSM) system. In contrast to previous SDSM systems for SMPs, the modified TreadMarks system uses POSIX threads for parallelism within an SMP node. This approach greatly simplifies the changes required to the SDSM in order to exploit the intranode hardware shared memory. We present performance results for seven applications (Barnes-Hut, CLU, and Water from SPLASH-2, 3D-FFT from NAS, Red-Black SOR, TSP, and MGS) running on an SP2 with four four-processor SMP nodes. A comparison between the thread implementation and the original implementation of TreadMarks shows that using the hardware shared memory within an SMP node significantly reduces the amount of data and the number of messages transmitted between nodes and consequently achieves speedups that are up to 30% better than the original versions. We also compare SDSM against message passing. Overall, the speedups of multithreaded TreadMarks programs are within 7?30% of the MPI versions.
6730998
Monitoring remotely executing shared memory programs in software DSMs
Peer-to-peer (P2P) cycle sharing over the Internet has become increasingly popular as a way to share idle cycles. A fundamental problem faced by P2P cycle sharing systems is how to incrementally monitor and verify, with low overhead, the execution of jobs submitted to a remote untrusted hosting machine, or cluster of machines. In this paper, we present the design and implementation of GripCop DSM, a novel incremental execution monitoring and verification scheme for software distributed shared memory (SDSM) programs running on remote clusters. Our scheme maximally leverages the shared memory abstraction provided by the SDSM system by extending the shared memory abstraction to the monitoring process by replicating one of the processes running on the host cluster to verify intermediate results at runtime. Our GripCop DSM employs two monitoring schemes: (i) a full-scale monitoring scheme that completely replicates the computation of a process running on the cluster; and (ii) a decoy monitoring scheme that deceives the host cluster into believing that full-scale monitoring is being performed without it ever actually being done, thereby incurring negligible overhead. Experiments show that the combined use of full-scale and decoy monitoring ensures faithful execution with low performance impact, even over a wide area network