DualMiner: A Dual-Pruning Algorithm for Itemsets with Constraints

Recently, constraint-based mining of itemsets for questions like find all frequent itemsets whose total price is at least $50 has attracted much attention. Two classes of constraints, monotone and antimonotone, have been very useful in this area. There exist algorithms that efficiently take advantage of either one of these two classes, but no previous algorithms can efficiently handle both types of constraints simultaneously. In this paper, we present DualMiner, the first algorithm that efficiently prunes its search space using both monotone and antimonotone constraints. We complement a theoretical analysis and proof of correctness of DualMiner with an experimental study that shows the efficacy of DualMiner compared to previous work.     

Characterizing files in the modern Gnutella network

The Internet has witnessed an explosive increase in the popularity of Peer-to-Peer (P2P) file-sharing applications during the past few years. As these applications become more popular, it becomes increasingly important to characterize their behavior in order to improve their performance and quantify their impact on the network. In this paper, we present a measurement study on characteristics of available files in the modern Gnutella system. We develop two new methodologies to capture accurate snapshots of available files in a large-scale P2P system. These methodologies were implemented in a parallel crawler that captures the entire overlay topology of the system where each peer in the overlay is annotated with its available files. We have captured more than 50 snapshots of the Gnutella system that span over 1 year period. Using these snapshots, we conduct three types of analysis on available files: (1) Static analysis, (2) Topological analysis, and (3) dynamic analysis. Our results reveal several interesting properties of available files in Gnutella that can be leveraged to improve the design and evaluation of P2P file-sharing applications. This paper extends and supplants the earlier version of this paper presented at MMCN 2006 [1]. This material is based upon work supported by the National Science Foundation (NSF) under Grant No. Nets-NBD-0627202, CAREER Award CNS-0448639, and an unrestricted gift from Cisco Systems. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF or Cisco.     

A new removable resist for high aspect ratio applications

A variety of different photo resists are used for fabrication of MEMS. Presently good results were reported for SU-8, a chemically amplified negative tone photoresist. But SU-8 has a disadvantage for some applications in LIGA technique, especially in the X-ray mask fabrication. After processing the finished resist pattern are hardly soluble from the substrate. This paper will briefly describe the current status of the development of the new negative tone photoresist CAR 44 whose big advantage is the easy removableness of the cross linked pattern. This work widely uses the contents of the presentation “A New Removable Resist for High Aspect Ratio Applications” to the High Aspect Ratio Micro Structure Technology workshop HARMST 2005 held in Gyeongyu (Republic of Korea), June 10–13, 2005.     

Crossover and mutation operators for grammar-guided genetic programming

This paper proposes a new grammar-guided genetic programming (GGGP) system by introducing two original genetic operators: crossover and mutation, which most influence the evolution process. The first, the so-called grammar-based crossover operator, strikes a good balance between search space exploration and exploitation capabilities and, therefore, enhances GGGP system performance. And the second is a grammar-based mutation operator, based on the crossover, which has been designed to generate individuals that match the syntactical constraints of the context-free grammar that defines the programs to be handled. The use of these operators together in the same GGGP system assures a higher convergence speed and less likelihood of getting trapped in local optima than other related approaches. These features are shown throughout the comparison of the results achieved by the proposed system with other important crossover and mutation methods in two experiments: a laboratory problem and the real-world task of breast cancer prognosis.     

Improving the Performance of Online Auctions Through Server-side Activity-based Caching

Online auction sites have very specific workloads and user behavior characteristics. Previous studies on workload characterization conducted by the authors showed that (1) bidding activity on auctions increases considerably after 90% of an auction’s life time has elapsed, (2) a very large percentage of auctions have a relatively low number of bids and bidders and a very small percentage of auctions have a high number of bids and bidders, (3) prices rise very fast after an auction has lasted more than 90% of its life time. Thus, if bidders are not able to successfully bid at the very last moments of an auction because of site overload, the final price may not be as high as it could be and sellers, and consequently the auction site, may lose revenue. In this paper, we propose server-side caching strategies in which cache placement and replacement policies are based on auction-related parameters such as number of bids placed or percent remaining time till closing time. A main-memory auction cache at the application server can be used to reduce accesses to the back-end database server. Trace-based simulations were used to evaluate these caching strategies in terms of cache hit ratio and cache efficiency. The performance characteristics of the best policies were then evaluated through experiments conducted on a benchmark online auction system.     

Inferring Phylogenetic Trees Using Answer Set Programming

We describe the reconstruction of a phylogeny for a set of taxa, with a character-based cladistics approach, in a declarative knowledge representation formalism, and show how to use computational methods of answer set programming to generate conjectures about the evolution of the given taxa. We have applied this computational method in two domains: historical analysis of languages and historical analysis of parasite-host systems. In particular, using this method, we have computed some plausible phylogenies for Chinese dialects, for Indo-European language groups, and for Alcataenia species. Some of these plausible phylogenies are different from the ones computed by other software. Using this method, we can easily describe domain-specific information (e.g., temporal and geographical constraints), and thus prevent the reconstruction of some phylogenies that are not plausible. This paper is a revised and extended version of [3].     

Robust H infinity control for networked systems with random packet losses.

In this paper, the robust H infinity control problem is considered for a class of networked systems with random communication packet losses. Because of the limited bandwidth of the channels, such random packet losses could occur, simultaneously, in the communication channels from the sensor to the controller and from the controller to the actuator. The random packet loss is assumed to obey the Bernoulli random binary distribution, and the parameter uncertainties are norm-bounded and enter into both the system and output matrices. In the presence of random packet losses, an observer-based feedback controller is designed to robustly exponentially stabilize the networked system in the sense of mean square and also achieve the prescribed H infinity disturbance-rejection-attenuation level. Both the stability-analysis and controller-synthesis problems are thoroughly investigated. It is shown that the controller-design problem under consideration is solvable if certain linear matrix inequalities (LMIs) are feasible. A simulation example is exploited to demonstrate the effectiveness of the proposed LMI approach.     

Neural network approach to background modeling for video object segmentation.

This paper presents a novel background modeling and subtraction approach for video object segmentation. A neural network (NN) architecture is proposed to form an unsupervised Bayesian classifier for this application domain. The constructed classifier efficiently handles the segmentation in natural-scene sequences with complex background motion and changes in illumination. The weights of the proposed NN serve as a model of the background and are temporally updated to reflect the observed statistics of background. The segmentation performance of the proposed NN is qualitatively and quantitatively examined and compared to two extant probabilistic object segmentation algorithms, based on a previously published test pool containing diverse surveillance-related sequences. The proposed algorithm is parallelized on a subpixel level and designed to enable efficient hardware implementation.     

A Review of Statistical Approaches to Level Set Segmentation: Integrating Color,Texture, Motion and Shape

Since their introduction as a means of front propagation and their first application to edge-based segmentation in the early 90’s, level set methods have become increasingly popular as a general framework for image segmentation. In this paper, we present a survey of a specific class of region-based level set segmentation methods and clarify how they can all be derived from a common statistical framework. Region-based segmentation schemes aim at partitioning the image domain by progressively fitting statistical models to the intensity, color, texture or motion in each of a set of regions. In contrast to edge-based schemes such as the classical Snakes, region-based methods tend to be less sensitive to noise. For typical images, the respective cost functionals tend to have less local minima which makes them particularly well-suited for local optimization methods such as the level set method. We detail a general statistical formulation for level set segmentation. Subsequently, we clarify how the integration of various low level criteria leads to a set of cost functionals. We point out relations between the different segmentation schemes. In experimental results, we demonstrate how the level set function is driven to partition the image plane into domains of coherent color, texture, dynamic texture or motion. Moreover, the Bayesian formulation allows to introduce prior shape knowledge into the level set method. We briefly review a number of advances in this domain.     

Progress in Web-based decision support technologies

World Wide Web technologies have transformed the design, development, implementation and deployment of decision support systems. This article reviews and summarizes recent technology developments, current usage of Web-based DSS, and trends in the deployment of such systems. Many firms use the Web as a medium to convey information about DSS products or to distribute DSS software. The use of Web-based computation to provide product demonstrations or to deploy DSS applications for remote access remains less common. The academic literature on Web-based DSS is largely focused on applications and implementations, and only a few articles examine architectural issues or provide design guidelines based on empirical evidence.