Constraints, specifically Separation of Duty (SoD) constraints, constitute an essential component for specifying Role Based Access Control (RBAC) policies. While it has been shown earlier that SoD constraints can be effectively represented using a set of t - t Statically Mutually Exclusive Roles (SMER) constraints, this paper presents a method for finding minimum number of users under multiple SMER constraints. We show that one way of solving the problem is to evaluate chromatic numbers for a set of graphs. However, since exhaustive search is computationally quite expensive, we present a genetic algorithm formulation of the problem. Each chromosome is a string of positive integers within a certain range and its length equals the number of t-t SMER constraints in the system. We report our results for different values of the number of roles and the number of constraints and also for different values of t.

Information Systems are needed for modernization of ICUs to deliver better health care services. EHR systems can improve the work flow management in health care delivery. This work proposes a secure web enabled system based on a multi-tier architecture for carrying out routine and special operations of Neonatal Intensive Care Unit (NICU). The system adopts a service oriented approach for execution of various tasks that are performed for managing NICU activities. It also facilitates decision support systems for a number of critical tasks of NICU. A prototype of the system has been installed in the neonatology department of SSKM Hospital, Kolkata, India and the staff of the hospital including doctors, nurses, laboratory personals and technicians are using it in a regular manner.

In the above titled paper (ibid., vol. 27, no. 9, pp. 1535-1544, Dec. 09), two figures were inaccurate due to a software programming error when implementing the path loss prediction algorithm. The corrected Figs. 4 and 6 are presented here. The correction does not otherwise affect results in the above paper.

Important soccer tournaments like the World Cup and the European Cup are broadcast to billions of people across the world. Therefore, advertising through billboards surrounding the playing arena in a soccer match is very attractive for promoting the brand image of a company. Analysis of strength and weakness of such an advertisement is helpful for the sponsors since a lot of money is spent on it and due to its potential impact on a large number of viewers at the same time. In this paper, we present a two-stage fuzzy system for analyzing the visual impact of advertisement billboards in soccer telecasts. In the first stage, visual impact of each shot is evaluated. Two parameters, namely, size and duration, of the billboards are considered in the fuzzy rules. After the shot level analysis, results are combined in the second stage to derive an overall visual impact. In both the stages, parameters of the fuzzy set membership functions are tuned using the Particle Swarm Optimization algorithm. The results have been compared against a user survey.

Abstract — In this paper, we propose an innovative approach for database intrusion detection which combines evidences from current as well as past behavior of users. It consists of four components, namely, rule-based component, belief combination component, security sensitive history database component and Bayesian learning component. The rule-based component consists of a set of well-defined rules which give independent evidences about a transaction’s behavior. An extension of Dempster-Shafer’s theory is used to combine multiple such evidences and an initial belief is computed. First level inferences are made about the transaction depending on this initial belief. Once the transaction is found to be suspicious, belief is updated according to its similarity with malicious or genuine transaction history using Bayesian learning. Experimental evaluation shows that the proposed intrusion detection system can effectively detect intrusive attacks in databases without raising too many false alarms. Index Terms − Database security, Dempster-Shafer theory, Bayesian learning, Intrusion detection, Suspicion score

Advertisement billboards placed along the periphery of a soccer field in popular tournaments are used to promote specific products or the brand image of a company. In this paper, we introduce a fuzzy logic based approach for estimating the visual impact of such billboards when broadcasted through the television medium. The present system estimates the persistence effect of a billboard on human mind by using a two stage fuzzy rule based system. In the first phase, a shot level analysis is carried out, which is followed by an inter shot analysis to estimate the overall impact. In both the stages, parameters of the fuzzy set membership functions are tuned using the particle swarm optimization algorithm. The system works on top of a billboard detection system and the results have been compared against a user survey.

We introduce the game of complex adaptive reasoning (CAR), as a self-correcting methodology for knowledge emergence in dialogue and narrative contexts of collaboration. Inspired by Holland's complex adaptive systems research (CAS), Revelator's CAR methodology is designed to model the complex logical relations among conjectures (represented in "If & then" rule form) that players articulate in game plays. The game's agent-based representation of these rule-form plays gives them adaptive capability, making it possible for "argument colonies" to emerge as possible knowledge to be tested. We are experimenting with natural language processing (NLP) query support for controlled natural language (CNL) formulation of game plays, to reveal knowledge formation in the conversational contexts of news blogs and research wikis. In Revelator's framework, knowledge is represented as continually emerging, not simply to be captured but to advance in a recursive process that improves human reasoning skills with appropriate support from technology.

A stochastic non-line-of-sight (NLOS) ultraviolet (UV) communication channel model is developed using a Monte Carlo simulation method based on photon tracing. The expected channel impulse response is obtained by computing photon arrival probabilities and associated propagation delay at the receiver. This method captures the multiple scattering effects of UV signal propagation in the atmosphere, and relaxes the assumptions of single scattering theory. The proposed model has a clear advantage in reliable prediction of NLOS path loss, as validated by outdoor experiments at small to medium elevation angles. A Gamma function is shown to agree well with the predicted impulse response, and this provides a simple means to determine the channel bandwidth. The developed model is employed to study the characteristics of NLOS UV scattering channels, including path loss and channel bandwidth, for a variety of scattering conditions, source wavelength, transmitter and receiver optical pointing geometries, and range.

A phenomenal growth in the number of credit card transactions, especially for online purchases, has recently led to a substantial rise in fraudulent activities. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. In real life, fraudulent transactions are interspersed with genuine transactions and simple pattern matching is not often sufficient to detect them accurately. Thus, there is a need for combining both anomaly detection as well as misuse detection techniques. In this paper, we propose to use two-stage sequence alignment in which a profile analyzer (PA) first determines the similarity of an incoming sequence of transactions on a given credit card with the genuine cardholder's past spending sequences. The unusual transactions traced by the profile analyzer are next passed on to a deviation analyzer (DA) for possible alignment with past fraudulent behavior. The final decision about the nature of a transaction is taken on the basis of the observations by these two analyzers. In order to achieve online response time for both PA and DA, we suggest a new approach for combining two sequence alignment algorithms BLAST and SSAHA.

Database protection mechanisms often fail to prevent occurrence of malicious transactions. In this paper, we consider the problem of database recovery from such committed malicious transactions in distributed database systems. In a database, the result of one transaction may affect the execution of some of the later transactions. This leads to damage spreading, which makes attack recovery even more complex. Traditional recovery schemes usually perform complete rollback to undo the effect of all the transactions, both malicious as well as non-malicious. We define several useful dependency relationships among transactions and present an algorithm to restore the consistency of a distributed database by negating the effect of only those transactions that are directly or transitively dependent on the malicious transactions.

The Internet has taken its place beside the telephone and the television as an important part of people's lives. Consumers rely on the Internet to shop, bank and invest online. Most online shoppers use credit cards to pay for their purchases. As credit card becomes the most popular mode of payment, cases of fraud associated with it are also increasing. In this paper, we model the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and show how it can be used for the detection of frauds. An HMM is trained with normal behavior of cardholder. If an incoming credit card transaction is not accepted by the HMM with sufficiently high probability, it is considered to be fraudulent. We present detailed experimental results to show the effectiveness of our approach.

In this paper, we propose a hierarchical state-based model for representing an echocardiogram video. It captures the semantics of video segments from dynamic characteristics of objects present in each segment. Our objective is to provide an effective method for segmenting an echo video into view, state, and substate levels. This is motivated by the need for building efficient indexing tools to support better content management. The modeling is done using four different views, namely, short axis, long axis, apical four chamber, and apical two chamber. For view classification, an artificial neural network is trained with the histogram of a region of interest of each video frame. Object states are detected with the help of synthetic M-mode images. In contrast to traditional single M-mode, we present a novel approach named sweep M-mode for state detection. We also introduce radial M-mode for substate identification from color flow Doppler 2-D imaging. The video model described here represents the semantics of video segments using first-order predicates. Suitable operators have been defined for querying the segments. We have carried out experiments on 20 echo videos and compared the results with manual annotation done by two experts. View classification accuracy is 97.19%. Misclassification error of the state detection stage is less than 13%, which is within acceptable range since only frames at the state boundaries are found to be misclassified.

In this paper, we propose a graph-based approach for detecting and tracking multiple players in broadcast soccer videos. In the first stage, the position of the players in each frame is determined by removing the non player regions. The remaining pixels are then grouped using a region growing algorithm to identify probable player candidates. A directed weighted graph is constructed, where probable player candidates correspond to the nodes of the graph while each edge links candidates in a frame with the candidates in next two consecutive frames. Finally, dynamic programming is applied to find the trajectory of each player. Experiments with several sequences from broadcasted videos of international soccer matches indicate that the proposed approach is able to track the players reasonably well even under varied illumination and ground conditions.

Revelator has been designed as a game for improving collaborative inquiry, in a format that resembles familiar intellectual games, such as bridge, crossword puzzles, poker, and chess. Its purpose is to reveal the complex relations among conditional propositions, which players use to represent their conjectures as plays in the game. Plays in Revelator become players' 'logical agents' that attempt to adapt within complex conceptual environments. Revelator's design provides a context for computer agent technologies (including Semantic Web, Conceptual Graphs, ISO Common Logic, and other approaches) to evolve in iterative cycles of empirical testing, evaluation, and advancement. The game can be used to challenge knowledge capture technology in tournaments to improve capabilities that support the progress of inquiry.

Traditional security mechanisms are often found to be inadequate for protection against attacks by authorized users or intruders posing as authorized users. This has drawn the interest of the research community towards intrusion detection techniques. We model the conflicting motives between an intruder and an intrusion detection system as a multistage game between two players, each trying to maximize its payoff. We consider the specific application of credit card fraud detection and propose a two-tiered architecture having a rule-based component in the first tier and a game-theoretic component in the second tier. Classical game theory is considered useful in many situations because it permits the formulation of strategies that are optimal, regardless of what the adversary does, negating the need for prediction of his/her behavior. However, we use it in a predictive application in the sense that we consider intruders as rational adversaries who would try to behave optimally, and the expected optimal behavior can be determined through game theory.

In this paper, we consider the problem of recovery from committed malicious transactions in distributed databases. We define several useful dependency relations among transactions and based on them present an online recovery scheme for restoring the consistency of a database

We introduce a fuzzy set theoretic approach for dealing with uncertainty in images in the context of spatial and topological relations existing among the objects in the image. We propose an object-oriented graph theoretic model for representing an image and this model allows us to assess the similarity between images using the concept of (fuzzy) graph matching. Sufficient flexibility has been provided in the similarity algorithm so that different features of an image may be independently focused upon.

Active database management systems (ADBMSs) use event-condition-action (ECA) rules. Each ECA rule specifies what action is to be taken when an event occurs and the specified condition is satisfied. In this paper, we introduce a concurrency control scheme for handling nested cooperative transactions using detached-mode ECA rules of an ADBMS. A state transition model has been proposed to specify different kinds of nested cooperative transactions using detached-mode ECA rules. The correctness criterion for concurrent execution of such nested cooperative transactions has been stated formally. The problem of verification of the correct schedules and a concurrency control mechanism have also been dealt with.

The present approaches to automated digital synthesis realise the control using a centralised controller. This paper presents a technique for synthesizing decentralised controllers based on interconnections between control and data path. The algorithm is based on analyzing the control flow and suitably partitioning it to realise decentralised controllers. The controllers, thus synthesized, preserve the control structure in the source description and have simple inter-controller communication. The algorithm is illustrated with an example and the controllers are mapped to pla like structures.

An algorithm to synthesize registers using multiport memories during data-path synthesis is presented. The proposed approach considers not only the access requirements of registers but also their interconnection to operators in order to minimize required interconnections. The same approach can be applied to select the optimum number of buses in a multibus architecture. The method is illustrated with an example.>

This paper deals with the application of fuzzy logic in a relational database environment with the objective of capturing more meaning of the data. It is shown that with suitable interpretations for the fuzzy membership functions, a fuzzy relational data model can be used to represent ambiguities in data values as well as impreciseness in the association among them. Relational operators for fuzzy relations have been studied, and applicability of fuzzy logic in capturing integrity constraints has been investigated. By introducing a fuzzy resemblance measure EQUAL for comparing domain values, the definition of classical functional dependency has been generalized to fuzzy functional dependency (ffd). The implication problem of ffds has been examined and a set of sound and complete inference axioms has been proposed. Next, the problem of lossless join decomposition of fuzzy relations for a given set of fuzzy functional dependencies is investigated. It is proved that with a suitable restriction on EQUAL, the design theory of a classical relational database with functional dependencies can be extended to fuzzy relations satisfying fuzzy functional dependencies.

The architecture of CONEX-expert system for control system design and analysis-is presented. The important features of CONEX are discussed and they include: a structured organization of the short-term database, a clustering criteria for partitioning the rule-base into connected rule-spaces, a language for representing the domain-specific rules, and an inference engine that can adopt different search strategies for different rule-spaces. Additionally, an explanation generation module gives CONEX the attribute of user friendliness.>

The paper deals with fuzzy functional dependencies and associated inference rules. A set of sound and complete inference rules are proposed. The problem of lossless join decomposition of fuzzy relations for a given set of fuzzy functional dependencies are examined.