27295065
Automatic extraction of titles from general documents using machine learning
In this paper, we propose a machine learning approach to title extraction from general documents. By general documents, we mean documents that can belong to any one of a number of specific genres, including presentations, book chapters, technical papers, brochures, reports, and letters. Previously, methods have been proposed mainly for title extraction from research papers. It has not been clear whether it could be possible to conduct automatic title extraction from general documents. As a case study, we consider extraction from Office including Word and PowerPoint. In our approach, we annotate titles in sample documents (for Word and PowerPoint, respectively) and take them as training data, train machine learning models, and perform title extraction using the trained models. Our method is unique in that we mainly utilize formatting information such as font size as features in the models. It turns out that the use of formatting information can lead to quite accurate extraction from general documents. Precision and recall for title extraction from Word are 0.810 and 0.837, respectively, and precision and recall for title extraction from PowerPoint are 0.875 and 0.895, respectively in an experiment on intranet data. Other important new findings in this work include that we can train models in one domain and apply them to other domains, and more surprisingly we can even train models in one language and apply them to other languages. Moreover, we can significantly improve search ranking results in document retrieval by using the extracted titles.
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).
5831663
Program counter-based prediction techniques for dynamic power management
Reducing energy consumption has become one of the major challenges in designing future computing systems. This paper proposes a novel idea of using program counters to predict I/O activities in the operating system. It presents a complete design of program-counter access predictor (PCAP) that dynamically learns the access patterns of applications and predicts when an I/O device can be shut down to save energy. PCAP uses path-based correlation to observe a particular sequence of program counters leading to each idle period and predicts future occurrences of that idle period. PCAP differs from previously proposed shutdown predictors in its ability to: 1) correlate I/O operations to particular behavior of the applications and users, 2) carry prediction information across multiple executions of the applications, and 3) attain higher energy savings while incurring lower mispredictions. We perform an extensive evaluation study of PCAP using a detailed trace-driven simulation and an actual Linux implementation. Our results show that PCAP achieves lower average mispredictions and higher energy savings than the simple timeout scheme and the state-of-the-art learning tree scheme
14073796
Salient Region Detection Using Weighted Feature Maps Based on the Human Visual Attention Model
Detection of salient regions in images is useful for object based image retrieval and browsing applications. This task can be done using methods based on the human visual attention model [1], where feature maps corresponding to color, intensity and orientation capture the corresponding salient regions. In this paper, we propose a strategy for combining the salient regions from the individual feature maps based on a new Composite Saliency Indicator (CSI) which measures the contribution of each feature map to saliency. The method also carries out a dynamic weighting of individual feature maps. The experiment results indicate that this combination strategy reflects the salient regions in an image more accurately.
5185627
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 memory system (SDSM). In contrast to previous SDSM systems for SMPs, the modified TreadMarks uses POSIX threads for parallelism within an SMP node. This approach greatly simplifies the changes required to the SDSM in order to exploit the intra-node hardware shared memory. We present performance results for six applications (SPLASH-2 Barnes-Hut and Water; NAS 3D-FFT, SOR, TSP and MGS) running on an SP2 with four four-processor SMP nodes. A comparison between the threaded 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 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.
10466292
Via-aware global routing for good VLSI manufacturability and high yield
CAD tools have become more and more important for integrated circuit (IC) design since a complicated system can be designed into a single chip, called system-on-a-chip (SOC), in which physical design tool is an essential and critical part. We try to consider the via minimization problem as early as possible in physical design. We propose a routing method focusing on minimizing vias while considering mutability and wire-length constraint. That is, in the global routing phase, we minimize the number of bends, which is closely related to the number of vias. Previous work only dealt with very small nets, but our algorithm is general for the nets with any size. Experimental results show that our algorithm can greatly reduce the count of bends for various sizes of nets while meeting the constraints of congestion and wire-length.
206501510
Head pose estimation using Fisher Manifold learning
Here, we propose a new learning strategy for head pose estimation. Our approach uses nonlinear interpolation to estimate the head pose using the learning result from face images of two head poses. Advantage of our method to regression method is that it only requires training images of two head poses and better generalization ability. It outperforms existed methods, such as regression and multiclass classification method, on both synthesis and real face images. Average head pose estimation error of yaw rotation is about 4/sup 0/, which proves that our method is effective in head pose estimation.