8794069
"Take One Get One Free": Leveraging P2P Networks for Content Promotion
The nature of digital content is undergoing a radical transformation due to the growing infusion of user-generated content. Users that generate content have a strong motivation to actively promote their content in order to achieve publicity, recognition or to simply spread their viewpoints, knowledge and creations. Existing content sharing mechanisms available to such users, such as in P2P file sharing, are pull-based, relegating the user to a passive role. To enable active participation from users, we propose Promoter, a framework that enhances P2P file sharing systems to support content promotion in addition to the available pull-based content search. Promotion is enabled by publicizing a user's content to peers and disseminating the content by incentivizing peers to download and further propagate it.
463081
Topology-Aware Routing in Structured Peer-to-Peer Overlay Networks
Structured peer-to-peer (p2p) overlay networks like CAN, Chord, Pastry and Tapestry offer a novel platform for a variety of scalable and decentralized distributed applications. They provide efficient and fault-tolerant routing, object location and load balancing within a self-organizing overlay network. One important aspect of these systems is how they exploit network proximity in the underlying Internet. We present a study of topology-aware routing approaches in p2p overlays, identify proximity neigbor selection as the most promising technique, and present an improved design in Pastry. Results obtained via analysis and via simulation of two large-scale topology models indicate that it is possible to efficiently exploit network proximity in self-organizing p2p substrates. Proximity neighbor selection incurs only a modest additional overhead for organizing and maintaining the overlay network. The resulting locality properties improve application performance and reduce network usage in the Internet substantially. Finally, we show that the impact of proximity neighbor selection on the load balancing in the p2p overlay is minimal.
1998178
A Hierarchical Approach to Internet Distance Prediction
Internet distance prediction gives pair-wise latency information with limited measurements. Recent studies have revealed that the quality of existing prediction mechanisms from the application perspective is short of satisfactory. In this paper, we explore the root causes and remedies for this problem. Our experience with different landmark selection schemes shows that although selecting nearby landmarks can increase the prediction accuracy for short distances, it can cause the prediction accuracy for longer distances to degrade. Such uneven prediction quality significantly impacts application performance. Instead of trying to select the landmark nodes in some "intelligent" fashion, we propose a hierarchical prediction approach with straightforward landmark selection. Hierarchical prediction utilizes multiple coordinate sets at multiple distance scales, with the "right" scale being chosen for prediction each time. Experiments with Internet measurement datasets show that this hierarchical approach is extremely promising for increasing the accuracy of network distance prediction.
481550
Improving Fine-Grained Irregular Shared-Memory Benchmarks by Data Reordering
We demonstrate that data reordering can substantially improve the performance of fine-grained irregular shared-memory benchmarks, on both hardware and software shared-memory systems. In particular, we evaluate two distinct data reordering techniques that seek to co-locate in memory objects that are in close proximity in the physical system modeled by the computation. The effects of these techniques are increased spatial locality and reduced false sharing. We evaluate the effectiveness of the data reordering techniques on a set of five irregular applications from SPLASH-2 and Chaos. We implement both techniques in a small library, allowing us to enable them in an application by adding less than 10 lines of code. Our results on one hardware and two software shared-memory systems show that, with data reordering during initialization, the performance of these applications is improved by 12%-99% on the Origin 2000, 30%-366% on TreadMarks, and 14%-269% on HLRC.
43789567
A Self-Organizing Flock of Condors
47427184
The Performance Impact of Kernel Prefetching on Buffer Cache Replacement Algorithms
A fundamental challenge in improving file system performance is to design effective block replacement algorithms to minimize buffer cache misses. Despite the well-known interactions between prefetching and caching, almost all buffer cache replacement algorithms have been proposed and studied comparatively, without taking into account file system prefetching, which exists in all modern operating systems. This paper shows that such kernel prefetching can have a significant impact on the relative performance in terms of the number of actual disk l/Os of many well-known replacement algorithms; it can not only narrow the performance gap but also change the relative performance benefits of different algorithms. Moreover, since prefetching can increase the number of blocks clustered for each disk I/O and, hence, the time to complete the I/O, the reduction in the number of disk l/Os may not translate into proportional reduction in the total I/O time. These results demonstrate the importance of buffer caching research taking file system prefetching into consideration and comparing the actual disk l/Os and the execution time under different replacement algorithms.
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.
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.
14006876
Perfect spline approximation
Our study of perfect spline approximation reveals: (i) it is closely related to ΣΔ modulation used in one-bit quantization of bandlimited signals. In fact, they share the same recursive formulae, although in different contexts; (ii) the best rate of approximation by perfect splines of order r with equidistant knots of mesh size h is hr-1. This rate is optimal in the sense that a function can be approximated with a better rate if and only if it is a polynomial of degree < r.The uniqueness of best approximation is studied, too. Along the way, we also give a result on an extremal problem, that is, among all perfect splines with integer knots on R, (multiples of) Euler splines have the smallest possible norms.
30629384
Ordering Unsymmetric Matrices into Bordered Block Diagonal Form for Parallel Processing
Large, sparse, unsymmetric systems of linear equations appear frequently in areas such as chemical engineering. One way of speeding up the solution of these linear systems is to solve them in parallel by reordering the unsymmetric matrix into a bordered block-diagonal (BBD) form. A multilevel ordering algorithm is presented in this paper. Numerical results of the algorithm are given to demonstrate that this algorithm gives better orderings than existing algorithms.
16350167
Efficient propagation for face annotation in family albums
In this paper, we propose and investigate a new user scenario for face annotation, in which users are allowed to multi-select a group of otogras and assign names to these otogras. The system will then attempt to propagate names from otogra level to face level, i.e. to infer the correspondence between name and face. Given the face similarity measure which combines methodologies from face recognition and content-based image retrieval, we formulate name propagation as an optimization problem. We define the objective function as the sum of similarities between each pair of faces of the same individual in different otogras, and propose an iterative optimization algorithm to infer the optimal correspondence. To make the propagation result reliable, a reject scheme is adopted to reject those with low confidence scores. Furthermore, we investigate the combination and alternation of browsing mode for propagation and viewer mode for annotation, so that each mode can benefit from additional inputs from the other mode. The experimental evaluation has been conducted within a typical family album of over one thousand otogras and the results show that the proposed approach is effective and efficient in automated face annotation in family albums.