CIRRIN [3] is a stylus based text input technique for mobile devices with a touch sensitive display. In this paper we explore the benefit of expanding the letters of CIRRIN to reduce the overall difficulty of selecting a letter. We adapted the existing CIRRIN to expand the characters as the stylus approached it to create a new text entry technique called expanding CIRRIN. In a small user study we compared the standard CIRRIN and expanding CIRRIN for different sentences. Our results indicate that expanding CIRRIN increases error rates and text input times. We observed that expanding the letters often made the stylus enter the CIRRIN ring adjacent to the intended letter, thereby increasing error rates. We discuss the implications of these results, and possible applications of expanding targets with other text input techniques such as the Metropolis [7] soft keyboard.

A scalable and expressive peer-to-peer (P2P) networking and computing framework requires efficient resource discovery services. Here we propose NEVRLATE, for Network-Efficient Vast Resource Lookup At The Edge, an efficient organization of directories or directory mirrors, providing a scalable distributed resource discovery service. NEVRLATE organizes directory servers in an approximate two-dimensional grid, or a set of sets of servers, for registration to occur in one 'horizontal' dimension, and lookup to occur in the other 'vertical' dimension. The payoff of organizing n servers into a structure like this is to achieve O(pn) message complexity for registration, and nearly constant complexity lookup. At extra cost NEVRLATE can provide fault tolerance, high availability and security, anonymity, and privacy. The protocol described can be seen as a way to organize Gnutella supernodes, or as a performance extension of Freenet's architecture. In addition, it supports expressive lookup mechanisms, and may provide a basis for a truly scalable worldwide infrastructure for the semantic and the extended web.

Despite advances in recent years in the area of mandatory access control in database systems, today's information repositories remain vulnerable to inference and data association attacks that can result in serious information leakage.Such information leakage can be prevented by properly classifying information according to constraints that express relationships among the security levels of data objects.In this paper we address the problem of classifying information by enforcing explicit data classification as well as inference and association constraints.We formulate the problem of determining a classification that ensures satisfaction of the constraints, while at the same time guaranteeing that information will not be unnecessarily overclassified.We present an approach to the s;olution of this problem and give an algorithm implementing it which is linear in simple cases, and low-order polynomial (n") in the general case.We also analyze a variant of the problem that is NP-hard. IntroductionMandatory policies control access to information on the basis of classifications, taken from a partially ordered set, assigned to data objects and subjects requesting access to them.Classifications assigned to information reflect the sensitivity of that information, while classifications assigned to subjects reflect their trustworthiness not to disclose the information they access to subjects not cleared to see it.By controlling read and write operations accordingly -allowing subjects to read information whose classification is dominated by their level #and write information only at a level that dominates theirs -mandatory policies provide a sim-

Although mandatory access control in database systems has been extensively studied in recent years, and several models and systems have been proposed, capabilities for enforcement of mandatory constraints remain limited. Lack of support for expressing and combating inference channels that improperly leak protected information remains a major limitation in today's multilevel systems. Moreover the working assumption that data are classified at insertion time makes previous approaches inapplicable to the classification of existing, possibly historical, data repositories that need to be classified for release. Such a capability would be of great benefit to, and appears to be in demand by, governmental, public and private institutions. We address the problem of classifying existing data repositories by taking into consideration explicit data classification as well as association and inference constraints. Constraints are expressed in a unified, DBMS- and model-independent framework, making the approach largely applicable. We introduce the concept of minimal classification as a labeling of data elements that while satisfying the constraints, ensures that no data element is classified at a level higher than necessary. We also describe a technique and present an algorithm for generating data classifications that are both minimal and preferred according to certain criteria. Our approach is based on preprocessing, or compiling, constraints to produce a set of simple classification assignments that can then be efficiently applied to classify any database instance.

Many analysis problems can be cast in the form of evaluating minimal models of a logic program. Although such formulations are appealing due to their simplicity and declarativeness, they have not been widely used in practice because, either existing logic programming systems do not guarantee completeness, or those that do have been viewed as too inefficient for integration into a compiler. The objective of this paper is to re-examine this issue in the context of recent advances in implementation technologies of logic programming systems.We find that such declarative formulations can indeed be used in practical systems, when combined with the appropriate tool for evaluation. We use existing formulations of analysis problems --- groundness analysis of logic programs, and strictness analysis of functional programs --- in this case study, and the XSB system, a table-based logic programming system, as the evaluation tool of choice. We give experimental evidence that the resultant groundness and strictness analysis systems are practical in terms of both time and space. In terms of implementation effort, the analyzers took less than 2 man-weeks (in total), to develop, optimize and evaluate. The analyzer itself consists of about 100 lines of tabled Prolog code and the entire system, including the components to read and preprocess input programs and to collect the analysis results, consists of about 500 lines of code.

The efficiency of resolution-based logic programming languages, such as Prolog, depends critically on selecting and executing sets of applicable clause heads to resolve against subgoals. Traditional approaches to this problem have focused on using indexing to determine the smallest possible applicable set. Despite their usefulness, these approaches ignore the nondeterminism inherent in many programming languages to the extent that they do not attempt to optimize execution after the applicable set has been determined. Unification factoring seeks to rectify this omission by regarding the indexing and unification phases of clause resolution as a single process. This article formalizes that process through the construction of factoring automata . A polynomial-time algorithm is given for constructing optimal factoring automata that preserve the clause selection strategy of Prolog. More generally, when the clause selection strategy is not fixed, constructing such an optimal automaton is shown to be NP-complete, solving an open trie minimization problem. Unification factoring is implemented through a source code transformation that preserves the full semantics of Prolog. This transformation is specified in the article, and using it, several well-known programs show significant performance improvements across several different systems. A prototype of unification factoring is available by anonymous ftp.

The efficiency of resolution-based logic programming languages, such as Prolog, depends critically on selecting and executing sets of applicable clause heads to resolve against subgoals. Traditional approaches to this problem have focused on using indexing to determine the smallest possible applicable set. Despite their usefulness, these approaches ignore the non-determinism inherent in many programming languages to the extent that they do not attempt to optimize execution after the applicable set theory has been determined.