18622586
State-based incremental testing of aspect-oriented programs
Taking aspects as incremental modifications to their base classes, this paper presents an incremental approach to testing whether or not aspect-oriented programs and their base classes conform to their respective behavior models. We exploit a rigorous aspect-oriented extension to state models for capturing the impact of aspects on the state transitions of base class objects as well as an explicit weaving mechanism for composing aspects into their base models. We generate abstract tests for base classes and aspect-oriented programs from their state models. As base class tests are not necessarily valid for aspect-oriented programs, we identify several rules for maximizing reuse of concrete base class tests for aspects according to the state-based impact of aspects on their base classes. To illustrate our approach, we use two examples that indicate distinctive types of aspect-oriented applications and exhibit fundamental features in complex applications: aspects removing state transitions from base classes and aspects adding and modifying state transitions in base classes. Our results show that majority of base class tests can be reused for aspects, but subtle modifications to some of them are necessary. In particular, positive (or negative) base class tests can become negative (or positive) aspect tests. We also discuss how several types of aspect-specific faults can be revealed by the state-based testing.
11698669
Modeling and verifying multi-agent behaviors using predicate/transition nets
In a multi-agent system, how agents accomplish a goal task is usually specified by multi-agent plans built from basic actions (e.g. operators) of which the agents are capable. A critical problem with such an approach is how can the designer make sure the plans are reliable. To tackle this problem, this paper presents a formal approach for modeling and analyzing multi-agent behaviors using Predicate/Transition (PrT) nets, a high-level formalism of Petri nets. We construct a multi-agent model by representing agent capabilities as transitions. To verify a multi-agent PrT model, we adapt the planning graphs as a compact structure for the reachability analysis. We also demonstrate that, based on the PrT model, whether parallel actions specified in multi-agent plans can be executed in parallel and whether the plans guarantee the achievement of the goal can be verified by analyzing the dependency relations among the transitions.
14562584
Integrating functional and security requirements with use case decomposition
Misuse case modeling is a viable option to depict the security requirements together with functional requirements. To investigate the interplay between functional and security requirements, this paper presents an approach to decomposing use cases, misuse cases, and mitigation use cases. We identify relationships among decomposed cases for each case type (use, misuse, mitigation use) and ensure consistency among the cases as decomposition occurs by properly modeling shared and optional cases. We also assign applicable actors to the decomposed cases. Decomposition is conducted for each case type independently and then integrated with the "threatens" and "mitigates" relationships. We provide processes for the proper use of the "threatens" relationship between misuse cases and use cases and the "mitigates" relationship between mitigation use cases and misuse cases at different levels of abstraction. Thus, a complete set of security-centric requirements can be specified from the project outset to guide subsequent software development phases
14135016
A Study on Illumination Invariant Face Recognition Methods Based on Multiple Eigenspaces
This paper presents two multiple illumination eigenspaces-based methods, RDEB and BPNNB, for solving the variable illumination problem of face recognition. The experiment shows that the methods have a high recognition ratio. In particular, BPNNB has outperformed the assumptive method which knows the illumination directions of faces and completes recognition in the specific eigenspace using eigenface method[2] for each face subset with a specific illumination direction.
11509547
Illumination invariant face recognition based on neural network ensemble
An illumination invariant face recognition method based on neural network ensemble architecture is proposed. Given a face image with an arbitrary illumination direction, it can complete recognition in a uniform way with high performance without knowing or estimating the illumination direction. Experimental result shows that the recognition ratio of the ensemble architecture is higher than the conventional approach that uses a single neural network to recognize faces of a specific illumination direction.
9746543
Design an Interoperable Mobile Agent System Based on Predicate Transition Net Models
Mobile agents provide an effective and flexible style to develop advanced distributed systems. In order to promote interoperability and ensure the quality of mobile agent systems, it is necessary to formalize software architecture of mobile agent systems. In this paper, we not only define the software architecture of interoperable mobile agent systems using predicate transition nets, but also analyze the interoperability between agents. In addition, a formal model-driven design method to develop mobile agent systems is described with examples. Our method naturally integrates formal methods and practical approaches in the agent system design phase. The method can be used to develop other complex software systems as well.
205336
A strategy for persistent object service under CORBA and Web environment
With the rapid development of the Internet, the integrated applications of Web, database, CORBA and Java have offered the three-tiered client/server application models a good computing environment and implementation strategy. This paper briefly introduces this kind of client/server application model based on CORBA and Web, and discusses a persistent object service for it. The paper describes in detail the persistent object service's architecture model, interactive process of components, main interface descriptions and some considerations when implementing this strategy. Finally, a summary is supplied for the persistent object service.
6114683
The evidential reasoning approach for MADA under both probabilistic and fuzzy uncertainties
Abstract Many multiple attribute decision analysis (MADA) problems are characterised by both quantitative and qualitative attributes with various types of uncertainties. Incompleteness (or ignorance) and vagueness (or fuzziness) are among the most common uncertainties in decision analysis. The evidential reasoning (ER) approach has been developed in the 1990s and in the recent years to support the solution of MADA problems with ignorance, a kind of probabilistic uncertainty. In this paper, the ER approach is further developed to deal with MADA problems with both probabilistic and fuzzy uncertainties. In this newly developed ER approach, precise data, ignorance and fuzziness are all modelled under the unified framework of a distributed fuzzy belief structure, leading to a fuzzy belief decision matrix. A utility-based grade match method is proposed to transform both numerical data and qualitative (fuzzy) assessment information of various formats into the fuzzy belief structure. A new fuzzy ER algorithm is developed to aggregate multiple attributes using the information contained in the fuzzy belief matrix, resulting in an aggregated fuzzy distributed assessment for each alternative. Different from the existing ER algorithm that is of a recursive nature, the new fuzzy ER algorithm provides an analytical means for combining all attributes without iteration, thus providing scope and flexibility for sensitivity analysis and optimisation. A numerical example is provided to illustrate the detailed implementation process of the new ER approach and its validity and wide applicability.
14141173
Distributed network simulations using the dynamic simulation backplane
Presents an approach for creating distributed, component-based simulations of communication networks by interconnecting models of sub-networks drawn from different network simulation packages. This approach supports the rapid construction of simulations for large networks by reusing existing models and software, and fast execution using parallel discrete event simulation techniques. A dynamic simulation backplane is proposed that provides a common format and protocol for message exchange, and services for transmitting data and synchronizing heterogeneous network simulation engines. In order to achieve plug-and-play interoperability, the backplane uses existing network communication standards and dynamically negotiates among the participant simulators to define a minimal subset of required information that each simulator must supply, as well as other optional information. The backplane then automatically creates a message format that can be understood by all participating simulators and dynamically creates the content of each message by using callbacks to the simulation engines. We describe our approach to interoperability as well as an implementation of the backplane. We present results that demonstrate the proper operation of the backplane by distributing a network simulation between two different simulation packages, ns2 and GloMoSim. Performance results show that the overhead for the creation of the dynamic messages is minimal. Although this work is specific to network simulations, we believe our methodology and approach can be used to achieve interoperability in other distributed computing applications as well.
17421892
Compiling real time functional reactive programming
Most of the past languages for reactive systems are based on synchronous dataflow. Recently, a new reactive language, called Real-Time Functional Reactive Programming (RT-FRP) [18] , has been proposed based on the functional paradigm. The unique feature of this language is the high-level abstraction provided in the form of behaviors for conti-nuous-time signals, and events for discrete-time signals. RT-FRP also features some performance guarantees in the form of bounded runtime and space usage for each reactive computation step.In this paper, we propose a new compilation scheme for RT-FRP. Our compilation scheme is based on two key stages. In the first stage, we translate RT-FRP program to an intermediate functional code. This code is deliberately kept at high level for two reasons. First, it is easier for us to validate its correctness. Second, it allows us to apply high-level source-to-source transformation to achieve further optimization. The second stage attempts to compile the intermediate code to a corresponding automata code. Our main novelty is the use of two high-level transformation techniques for this compilation. The first technique, partial evaluation, attempts to propagate constant values (wherever feasible) in order to perform more aggressive specialization. The second technique, tupling, combines mutually dependent automata together into a composite automaton whenever possible. Both techniques are needed for generating fast target code for RT-FRP.
19364030
The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees
Abstract Multiple attribute decision analysis (MADA) problems having both quantitative and qualitative attributes under uncertainty can be modeled using the evidential reasoning (ER) approach. Several types of uncertainties such as ignorance and fuzziness can be modeled in the ER framework. In this paper, the ER approach will be extended to model new types of uncertainties including interval belief degrees and interval data that could be incurred in decision situations such as group decision making. The Dempster–Shafer (D–S) theory of evidence is first extended, which is one of the bases of the ER approach. The analytical ER algorithm is used to combine all evidence simultaneously. Two pairs of nonlinear optimization models are constructed to estimate the upper and lower bounds of the combined belief degrees and to compute the maximum and the minimum expected utilities of each alternative, respectively. Interval data are equivalently transformed to interval belief degrees and are incorporated into the nonlinear optimization models. A cargo ship selection problem is examined to show the implementation process of the proposed approach.