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226
So Far (Schematically) yet so Near (Semantically)
, 1992
"... In a multidatabase system, schematic conflicts between two objects are usually of interest only when the objects have some semantic affinity. In this paper we try to reconcile the two perspectives. We first define the concept of semantic proximity and provide a semantic taxonomy. We then enumerate a ..."
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Cited by 100 (2 self)
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In a multidatabase system, schematic conflicts between two objects are usually of interest only when the objects have some semantic affinity. In this paper we try to reconcile the two perspectives. We first define the concept of semantic proximity and provide a semantic taxonomy. We then enumerate and classify the schematic and data conflicts. We discuss possible semantic similarities between two objects that have various types of schematic and data conflicts. Issues of uncertain information and inconsistent information are also addressed.
From Computing With Numbers To Computing With Words From Manipulation Of Measurements To Manipulation of Perceptions
 Appl. Math. Comput. Sci
"... Computing, in its usual sense, is centered on manipulation of numbers and symbols. In contrast, computing with words, or CW for short, is a methodology in which the objects of computation are words and propositions drawn from a natural language, e.g., small, large, far, heavy, not very likely, the p ..."
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Cited by 89 (3 self)
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Computing, in its usual sense, is centered on manipulation of numbers and symbols. In contrast, computing with words, or CW for short, is a methodology in which the objects of computation are words and propositions drawn from a natural language, e.g., small, large, far, heavy, not very likely, the price of gas is low and declining, Berkeley is near San Francisco, it is very unlikely that there will be a significant increase in the price of oil in the near future, etc. Computing with words is inspired by the remarkable human capability to perform a wide variety of physical and mental tasks without any measurements and any computations. Familiar examples of such tasks are parking a car, driving in heavy traffic, playing golf, riding a bicycle, understanding speech and summarizing a story. Underlying this remarkable capability is the brain’s crucial ability to manipulate perceptions – perceptions of distance, size, weight, color, speed, time, direction, force, number, truth, likelihood and other characteristics of physical and mental objects. Manipulation of perceptions plays a key role in human recognition, decision and execution processes. As a methodology, computing with words provides a foundation for a computational theory of perceptions – a theory which may have an important bearing on how humans make – and machines might make – perceptionbased rational decisions in an environment of imprecision, uncertainty and partial truth. A basic difference between perceptions and measurements is that, in general, measurements are crisp whereas perceptions are fuzzy. One of the fundamental aims of science has been and continues to be that of progressing from perceptions to measurements. Pursuit of this aim has led to brilliant successes. We have sent men to the moon; we can build computers
Perspectives on the Theory and Practice of Belief Functions
 International Journal of Approximate Reasoning
, 1990
"... The theory of belief functions provides one way to use mathematical probability in subjective judgment. It is a generalization of the Bayesian theory of subjective probability. When we use the Bayesian theory to quantify judgments about a question, we must assign probabilities to the possible answer ..."
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Cited by 86 (7 self)
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The theory of belief functions provides one way to use mathematical probability in subjective judgment. It is a generalization of the Bayesian theory of subjective probability. When we use the Bayesian theory to quantify judgments about a question, we must assign probabilities to the possible answers to that question. The theory of belief functions is more flexible; it allows us to derive degrees of belief for a question from probabilities for a related question. These degrees of belief may or may not have the mathematical properties of probabilities; how much they differ from probabilities will depend on how closely the two questions are related. Examples of what we would now call belieffunction reasoning can be found in the late seventeenth and early eighteenth centuries, well before Bayesian ideas were developed. In 1689, George Hooper gave rules for combining testimony that can be recognized as special cases of Dempster's rule for combining belief functions (Shafer 1986a). Similar rules were formulated by Jakob Bernoulli in his Ars Conjectandi, published posthumously in 1713, and by JohannHeinrich Lambert in his Neues Organon, published in 1764 (Shafer 1978). Examples of belieffunction reasoning can also be found in more recent work, by authors
A Robust Competitive Clustering Algorithm with Applications in Computer Vision
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1998
"... This paper addresses three major issues associated with conventional partitional clustering, namely, sensitivity to initialization, difficulty in determining the number of clusters, and sensitivity to noise and outliers. The proposed Robust Competitive Agglomeration (RCA) algorithm starts with a lar ..."
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Cited by 81 (3 self)
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This paper addresses three major issues associated with conventional partitional clustering, namely, sensitivity to initialization, difficulty in determining the number of clusters, and sensitivity to noise and outliers. The proposed Robust Competitive Agglomeration (RCA) algorithm starts with a large number of clusters to reduce the sensitivity to initialization, and determines the actual number of clusters by a process of competitive agglomeration. Noise immunity is achieved by incorporating concepts from robust statistics into the algorithm. RCA assigns two different sets of weights for each data point: the first set of constrained weights represents degrees of sharing, and is used to create a competitive environment and to generate a fuzzy partition of the data set. The second set corresponds to robust weights, and is used to obtain robust estimates of the cluster prototypes. By choosing an appropriate distance measure in the objective function, RCA can be used to find a...
Possibility theory in constraint satisfaction problems: Handling priority, preference and uncertainty
 Applied Intelligence
, 1996
"... In classical Constraint Satisfaction Problems (CSPs) knowledge is embedded in a set of hard constraints, each one restricting the possible values of a set of variables. However constraints in real world problems are seldom hard, and CSP's are often idealizations that do not account for the preferenc ..."
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Cited by 74 (13 self)
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In classical Constraint Satisfaction Problems (CSPs) knowledge is embedded in a set of hard constraints, each one restricting the possible values of a set of variables. However constraints in real world problems are seldom hard, and CSP's are often idealizations that do not account for the preference among feasible solutions. Moreover some constraints may have priority over others. Lastly, constraints may involve uncertain parameters. This paper advocates the use of fuzzy sets and possibility theory as a realistic approach for the representation of these three aspects. Fuzzy constraints encompass both preference relations among possible instanciations and priorities among constraints. In a Fuzzy Constraint Satisfaction Problem (FCSP), a constraint is satisfied to a degree (rather than satisfied or not satisfied) and the acceptability of a potential solution becomes a gradual notion. Even if the FCSP is partially inconsistent, best instanciations are provided owing to the relaxation of ...
Fuzzy functional dependencies and lossless join decomposition of fuzzy relational database systems
 ACM Transactions on Database Systems
, 1988
"... 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 ..."
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Cited by 73 (0 self)
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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.
Blending Reactivity and GoalDirectedness in a Fuzzy Controller
 In Proc. of the 2nd IEEE Int. Conf. on Fuzzy Systems
, 1993
"... Controlling the movement of an autonomous mobile robot requires the ability to pursue strategic goals in a highly reactive way. We describe a fuzzy controller for such a mobile robot that can take abstract goals into consideration. Through the use of fuzzy logic, reactive behavior (e.g., avoiding ob ..."
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Cited by 72 (17 self)
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Controlling the movement of an autonomous mobile robot requires the ability to pursue strategic goals in a highly reactive way. We describe a fuzzy controller for such a mobile robot that can take abstract goals into consideration. Through the use of fuzzy logic, reactive behavior (e.g., avoiding obstacles on the way) and goaloriented behavior (e.g., trying to reach a given location) are smoothly blended into one sequence of control actions. The fuzzy controller has been implemented on the SRI robot Flakey. I. Introduction Autonomous operation of a mobile robot in a real environment poses a series of problems. In the general case, knowledge of the environment is partial and approximate; sensing is noisy; the dynamics of the environment can only be partially predicted; and robot's hardware execution is not completely reliable. Though, the robot must take decisions and execute actions at the timescale of the environment. Classical planning approaches have been criticized for not being...
Nonmonotonic Reasoning, Conditional Objects and Possibility Theory
 Artificial Intelligence
, 1997
"... . This short paper relates the conditional objectbased and possibility theorybased approaches for reasoning with conditional statements pervaded with exceptions, to other methods in nonmonotonic reasoning which have been independently proposed: namely, Lehmann's preferential and rational closure en ..."
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Cited by 68 (17 self)
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. This short paper relates the conditional objectbased and possibility theorybased approaches for reasoning with conditional statements pervaded with exceptions, to other methods in nonmonotonic reasoning which have been independently proposed: namely, Lehmann's preferential and rational closure entailments which obey normative postulates, the infinitesimal probability approach, and the conditional (modal) logicsbased approach. All these methods are shown to be equivalent with respect to their capabilities for reasoning with conditional knowledge although they are based on different modeling frameworks. It thus provides a unified understanding of nonmonotonic consequence relations. More particularly, conditional objects, a purely qualitative counterpart to conditional probabilities, offer a very simple semantics, based on a 3valued calculus, for the preferential entailment, while in the purely ordinal setting of possibility theory both the preferential and the rational closure entai...
Cerberus: A ContextAware Security Scheme for Smart Spaces
"... Ubiquitous computing has fueled the idea of constructing sentient, informationrich "smart spaces" that extend the boundaries of traditional computing to encompass physical spaces, embedded devices, sensors, and other machinery. To achieve this, smart spaces need to capture situational information s ..."
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Cited by 44 (3 self)
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Ubiquitous computing has fueled the idea of constructing sentient, informationrich "smart spaces" that extend the boundaries of traditional computing to encompass physical spaces, embedded devices, sensors, and other machinery. To achieve this, smart spaces need to capture situational information so that they can detect changes in context and adapt themselves accordingly. However, without considering basic security issues ubiquitous computing environments could be rife with vulnerabilities. Ubiquitous computing environments impose new requirements on security. Security services, like authentica tion and access control, have to be nonintrusive, intelli gent, and able to adapt to the rapidly changing contexts of the spaces. We present a ubiquitous security mechanism that integrates contextawareness with automated reasoning to perform authentication and access control in ubiquitous computing environments.