Abstract

Sketch matching algorithms are commonly used for indexing and retrieval of documents based on printed or hand-drawn sketches. One could use a hand-held computer to do sketch-based queries to a database containing hand-drawn and printed sketches. We present an on-line hand-drawn sketch matching algorithm based on a line-based representation of sketches. A distance measure is defined for comparing two sketches based on this representation. The algorithm is computationally efficient and achieves a recall rate of 88.44% at the same precision, when tested on a database of 150 sketches collected from 5 users.

Abstract

This paper describes a new approach for script separation. A characterlevel script separation scheme is combined with a Viterbi algorithm to get an optimal sequence of scripts which could generate such a text. This method complements the popular approaches for script separation at paragraph level using texture features or at line level using structural features.

Abstract

The blocking phenomena in two-phase commit (2PC) reduces the availability of the system as the blocked transactionskeep all the resources until the recovery of the coordinator. The three-phase commit (3PC) protocol involves an extra roundof message transmission to resolve the blocking problem. In this paper, we propose a backup commit (BC) protocol to reducethe blocking problem by attaching multiple backup sites to the coordinator site. In BC, after receiving responses from theparticipants, the coordinator quickly communicates the final decision to the backup sites, before it sends the final decision tothe participants. When blocking occurs, the participant sites can terminate the transaction by consulting a backup site of thecoordinator. The BC protocol resolves the blocking in most of the coordinator site failures without involving an expensivecommunication cycle as in 3PC. The simulation experiments indicate that the throughput performance of BC is close to 2PC.2002 Elsevier Science B.V. All rights reserved.

Abstract

Distributed locking is commonly adopted for performing concurrency control in distributed systems. It incorporatesadditional steps for handling deadlocks. This activity is carried out by methods based on wait-for-graphs or probes. The present studyexamines detection of conflicts based on enhanced local processing for distributed concurrency control. In the proposed “edgedetection” approach, a graph-based resolution of access conflicts has been adopted. The technique generates a uniform wait-forprecedence order at distributed sites for transactions to execute. The earlier methods based on serialization graph testing are difficultto implement in a distributed environment. The edge detection approach is a fully distributed approach. It presents a unified techniquefor locking and deadlock detection exercises. The technique eliminates many deadlocks without incurring message overheads

Abstract

Insect pests are a major cause of crop loss globally. Pest management will be effective and efficient if we can predict the occurrence of peak activities of a given pest. Research efforts are going on to understand the pest dynamics by applying analytical and other techniques on pest surveillance data sets. In this study we make an effort to understand pest population dynamics using Neural Networks by analyzing pest surveillance data set of Helicoverpa armigera or Pod borer on chickpea (Cicer arietinum L.) crop. The results show that neural network method successfully predicts the pest attack incidences for one week in advance.

Abstract

The output of boolean association rule mining algorithms is often too large for manual examination. For dense datasets, it is often impractical to even generate all frequent itemsets. The closed itemset approach handles this information overload by pruning “uninteresting” rules following the observation that most rules can be derived from other rules. In this paper, we propose a new framework, namely, the generalized closed (or g-closed) itemset framework. By allowing for a small tolerance in the accuracy of itemset supports, we show that the number of such redundant rules is far more than what was previously estimated. Our scheme can be integrated into both levelwise algorithms (Apriori) and two-pass algorithms (ARMOR). We evaluate its performance by measuring the reduction in output size as well as in response time. Our experiments show that incorporating gclosed itemsets provides significan

Abstract

The output of boolean association rule mining algorithms is often too large for manual examination. For dense datasets, it is often impractical to even generate all frequent itemsets. The closed itemset approach handles this information overload by pruning “uninteresting” rules following the observation that most rules can be derived from other rules. In this paper, we propose a new framework, namely, the generalized closed (or
-closed) itemset framework. By allowing for a small tolerance in the accuracy of itemset supports, we show that the number of such redundant rules is far more than what was previously estimated. Our scheme can be integrated into both levelwise algorithms (Apriori) and two-pass algorithms (ARMOR). We evaluate its performance by measuring the reduction in output size as well as in response time. Our experiments show that incorporating g-closed itemsets provides significant performance improvements on a variety of databases.

Abstract

In this paper, we first focus our attention on the question of how much space remains for performance improvement over current association rule mining algorithms. Our strategy is to compare their performance against an “Oracle algorithm” that knows in advance the identities of all frequent itemsets in the database and only needs to gather their actual supports to complete the mining process. Our experimental results show that current mining algorithms do not perform uniformly well with respect to the Oracle for all database characteristics and support thresholds. In many cases there is a substantial gap between the Oracle’s performance and that of the current mining algorithms. Second, we present a new mining algorithm, called ARMOR, that is constructed by making minimal changes to the Oracle algorithm. ARMOR consistently performs within a factor of two of the Oracle on both real and synthetic datasets over practical ranges of support specifications.

Abstract

Most natural spatiotemporal systems are an organized ensemble of dynamical subsystems whose behaviour is regulated by nonlinearly coupled multi-variable processes. The response of each of these variables and/or combinations of them to any single or composite external perturbation can influence its spatiotemporal behaviour in a complex and non-intuitive manner. This paper attempts to study the dynamical response of two coupled map lattice systems having local dynamics described by (a) coupled discrete maps, and (b) coupled differential equations, to external perturbation (or “pinning”) applied to each variable individually or simultaneously. We show that the response of different variables to external pinning is quite different. Our results indicate that, though this pinning approach is useful in controlling complex dynamics both globally and locally, enhancing complexity in dynamics (“anti-control”) is …

Abstract

In this paper we propose an approach to extract community structures in the Web by considering a community structure as a group of content creators that manifests itself as a set of interlinked pages. We abstract the community structure as a dense bipartite graph (DBG) over a group of Web pages and proposed an algorithm to extract the DBGs from the given data set. Also, a high-level community is abstracted as a DBG over a set of a low-level communities. Using the proposed approach, a community hierarchy can be constructed for the given data set that generalizes a large number of low-level communities into a few highlevel communities. Using the proposed approach, we have extracted a a three-level community hierarchy from 10 GB TPEC (Text PEtrieval Conference) data set. We believe that the extracted community hierarchy facilitates easy analysis of the low-level communities, helps in reorganizing the Web sites, and provides a way to understand the sociology of the Web