10852915
Cooperative Caching with Optimal Radius in Hybrid Wireless Networks
In this paper, we propose a novel caching scheme, called energy efficient cooperative caching with optimal radius (ECOR), to overcome the limitations of hybrid wireless networks such as high energy consumption, long access latency and poor load balance. In ECOR, each mobile user (MU) forms a cooperation zone with MUs in proximity to share cached data items. An analytical model of ECOR is developed to determine the optimal radius of the cooperation zone based on MU’s location, data popularity and node density. Experimental results show that ECOR achieves significant performance improvement in power saving, network throughput and load balance in comparison with other existing approaches.
1571784
QoS middleware support for pervasive computing applications
Today, pervasive computing technologies are being developed to provide automated, real-time, continual, and unobtrusive user services in dynamic heterogeneous environments such as telemedicine, manufacturing, space endeavors, crisis management, and military. However, the full potential of pervasive computing cannot be realized without enabling middleware technologies - henceforth referred to as "middleware services"; i.e., they provide services for high level applications. In this paper, we introduce middleware services that are required to address the challenges related to adapting to dynamically changing situations, meeting communication QoS requirements, and achieving scalability in large-scale pervasive computing applications over heterogeneous network infrastructures. Our approach in developing middleware support for pervasive computing is based on the community computing concept, which provides us with a unified method to dynamically integrate the middleware services with each other and with high level applications on a just-in-time basis. The proposed middleware services for pervasive computing are highly modular, lightweight, and easily deployable to meet the needs of pervasive computing applications in networked environments. In this paper, we describe our proposed QoS algorithms and techniques for synergistic integration of middleware services into pervasive computing applications. In addition, we propose new performance metrics and a benchmark suite approach to evaluate middleware services in pervasive computing. Finally, we present some early prototype results from our proof of concept implementation.
5533650
The hierarchical cliques interconnection network
The fully connected network possesses extremely good topological, fault-tolerant, and embedding properties. However, due to its high degree, the fully connected network has not been an attractive candidate for building parallel computers. On the other hand, tree-based networks are popular as parallel computer networks, even though they suffer from poor fault-tolerance and embedding properties. The hierarchical cliques interconnection network described in this paper incorporates positive features of the fully connected network and the tree network. In other words, the hierarchical cliques possess such desirable properties as low diameter, low degree, self routing, versatile embedding, good fault-tolerance and strong resilience. Hierarchical cliques can efficiently embed most important networks and possess a scalable, modular structure. Further, by combining hierarchical cliques with fat trees congestion in the upper levels can be alleviated.
2464851
Fast Memory-Efficient Generalized Belief Propagation
Generalized Belief Propagation (gbp) has proven to be a promising technique for performing inference on Markov random fields (mrfs). However, its heavy computational cost and large memory requirements have restricted its application to problems with small state spaces. We present methods for reducing both run time and storage needed by gbp for a large class of pairwise potentials of the mrf. Further, we show how the problem of subgraph matching can be formulated using this class of mrfs and thus, solved efficiently using our approach. Our results significantly outperform the state-of-the-art method. We also obtain excellent results for the related problem of matching pictorial structures for object recognition.
14151624
Solving Markov Random Fields using Second Order Cone Programming Relaxations
This paper presents a generic method for solvingMarkov random fields (MRF) by formulating the problem of MAP estimation as 0-1 quadratic programming (QP). Though in general solving MRFs is NP-hard, we propose a second order cone programming relaxation scheme which solves a closely related (convex) approximation. In terms of computational efficiency, our method significantly outperforms the semidefinite relaxations previously used whilst providing equally (or even more) accurate results. Unlike popular inference schemes such as Belief Propagation and Graph Cuts, convergence is guaranteed within a small number of iterations. Furthermore, we also present a method for greatly reducing the runtime and increasing the accuracy of our approach for a large and useful class of MRFs. We compare our approach with the state-of-the-art methods for subgraph matching and object recognition and demonstrate significant improvements.
7847540
Electronic Multidimensional Auctions and the Role of Information Feedback
* Part of this work was done while the first author was visiting the IBM T.J. Watson Research Center in Hawthorne, NY, whose hospitality is gratefully acknowledged. Anant Jhingran and Sunil Noronha and especially Mike Rothkopf provided valuable feedback on earlier versions. However, any remaining errors are solely the responsibility of the authors. AbstractTraditionally, companies aiming to achieve competition among suppliers have used sealed bidding procedures in their sourcing processes. The advances in information technology and in particular the Internet now allow these companies to use different and more complex auction mechanisms. In particular multidimensional auctions are a natural extension of the standard sealed-bid auctions, but these auctions raise a whole host of issues that have been little investigated. In this article we focus on one of these issues, namely the role of information feedback given during the auction process. We describe various feedback policies and analyze the expected impact on the performance of the auction mechanism using the criteria of speed of convergence, allocative efficiency and Pareto optimality. This can help both researchers and practitioners in a more detailed and thorough analysis of electronic auctions.
14783291
A large-vocabulary continuous speech recognition system for Hindi
In this paper we present two new techniques that have been used to build a large-vocabulary continuous Hindi speech recognition system. We present a technique for fast bootstrapping of initial phone models of a new language. The training data for the new language is aligned using an existing speech recognition engine for another language. This aligned data is used to obtain the initial acoustic models for the phones of the new language. Following this approach requires less training data. We also present a technique for generating baseforms (phonetic spellings) for phonetic languages such as Hindi. As is inherent in phonetic languages, rules generally capture the mapping of spelling to phonemes very well. However, deep linguistic knowledge is required to write all possible rules, and there are some ambiguities in the language that are difficult to capture with rules. On the other hand, pure statistical techniques for base and generation require large amounts of training data that are not readily available. We propose a hybrid approach that combines rule-based and statistical approaches in a two-step fashion. We evaluate the performance of the proposed approaches through various phonetic classification and recognition experiments.
7854172
OBJ CUT
In this paper, we present a principled Bayesian method for detecting and segmenting instances of a particular object category within an image, providing a coherent methodology for combining top down and bottom up cues. The work draws together two powerful formulations: pictorial structures (PS) and Markov random fields (MRFs) both of which have efficient algorithms for their solution. The resulting combination, which we call the object category specific MRF, suggests a solution to the problem that has long dogged MRFs namely that they provide a poor prior for specific shapes. In contrast, our model provides a prior that is global across the image plane using the PS. We develop an efficient method, OBJ CUT, to obtain segmentations using this model. Novel aspects of this method include an efficient algorithm for sampling the PS model, and the observation that the expected log likelihood of the model can be increased by a single graph cut. Results are presented on two object categories, cows and horses. We compare our methods to the state of the art in object category specific image segmentation and demonstrate significant improvements.