A constraint-based approach to spatiotemporal reasoning

We introduce a form of spatiotemporal reasoning that uses homogeneous representations of time and the three dimensions of space. The basis of our approach is Allen's temporal logic on the one hand and general constraint satisfaction algorithms on the other, where we present a new view of constraint reasoning to cope with the affordances of spatiotemporal reasoning as introduced here. As a realization for constraint reasoning, we suggest a massively parallel implementation in form of Boltzmann machines.     

A new constructive approach to constraint-based geometric design

In the paper, a new constructive approach to solving geometric constraints in 2-D space is presented. Constraints are employed on lines and points only, but more sophisticated geometric elements like Bézier curves and ellipses can also be constrained by mapping them onto auxiliary lines and points. The algorithm is based on local propagation, but first, the problem is transformed into a form that guarantees success of employing this simple technique. The most important steps are substitution of complex constraints with sets of simpler ones and insertion of redundant constraints by solving triangles and determining sums and differences of adjacent angles. In this way, various well-constrained problems with a few exceptions are solved, over-constrained scenes and input data contradictory to some well-known mathematical theorems are detected, and the algorithm is proved successful in many under-constrained cases as well.     

A constraint-based approach to shape management in multimedia databases

Shape management is an important functionality in multimedia databases. Shape information can be used in both image acquisition and image retrieval. Several approaches have been proposed to deal with shape representation and matching. Among them, the data-driven approach supports searches for shapes based on indexing techniques. Unfortunately, efficient data-driven approaches are often defined only for specific types of shape. This is not sufficient in contexts in which arbitrary shapes should be represented. Constraint databases use mathematical theories to finitely represent infinite sets of relational tuples. They have been proved to be very useful in modeling spatial objects. In this paper, we apply constraint-based data models to the problem of shape management in multimedia databases. We first present the constraint model and some constraint languages. Then, we show how constraints can be used to model general shapes. The use of a constraint language as an internal specification and execution language for querying shapes is also discussed. Finally, we show how a constraint database system can be used to efficiently retrieve shapes, retaining the advantages of the already defined approaches.     

A hybrid heuristic approach to optimize rule-based software quality estimation models

Software quality is defined as the degree to which a software component or system meets specified requirements and specifications. Assessing software quality in the early stages of design and development is crucial as it helps reduce effort, time and money. However, the task is difficult since most software quality characteristics (such as maintainability, reliability and reusability) cannot be directly and objectively measured before the software product is deployed and used for a certain period of time. Nonetheless, these software quality characteristics can be predicted from other measurable software quality attributes such as complexity and inheritance. Many metrics have been proposed for this purpose. In this context, we speak of estimating software quality characteristics from measurable attributes. For this purpose, software quality estimation models have been widely used. These take different forms: statistical models, rule-based models and decision trees. However, data used to build such models is scarce in the domain of software quality. As a result, the accuracy of the built estimation models deteriorates when they are used to predict the quality of new software components. In this paper, we propose a search-based software engineering approach to improve the prediction accuracy of software quality estimation models by adapting them to new unseen software products. The method has been implemented and favorable result comparisons are reported in this work.     

Uncertainty representation in software models: a survey

Software and Systems Modeling - This paper provides a comprehensive overview and analysis of research work on how uncertainty is currently represented in software models. The survey presents the...     

A fuzzy constraint-based approach to data reconciliation in material flow analysis

Data reconciliation consists in modifying noisy or unreliable data in order to make them consistent with a mathematical model (herein a material flow network). The conventional approach relies on least-squares minimization. Here, we use a fuzzy set-based approach, replacing Gaussian likelihood functions by fuzzy intervals, and a leximin criterion. We show that the setting of fuzzy sets provides a generalized approach to the choice of estimated values, that is more flexible and less dependent on oftentimes debatable probabilistic justifications. It potentially encompasses interval-based formulations and the least squares method, by choosing appropriate membership functions and aggregation operations. This paper also lays bare the fact that data reconciliation under the fuzzy set approach is viewed as an information fusion problem, as opposed to the statistical tradition which solves an estimation problem.     

A constraint-based topic modeling approach for name disambiguation

Name ambiguity refers to a problem that different people might be referenced with an identical name. This problem has become critical in many applications, particularly in online bibliography systems, such as DBLP and CiterSeer. Although much work has been conducted to address this problem, there still exist many challenges. In this paper, a general framework of constraint-based topic modeling is proposed, which can make use of user-defined constraints to enhance the performance of name disambiguation. A Gibbs sampling algorithm that integrates the constraints has been proposed to do the inference of the topic model. Experimental results on a real-world dataset show that significant improvements can be obtained by taking the proposed approach.     

A constraint-based topic modeling approach for name disambiguation

Name ambiguity refers to a problem that different people might be referenced with an identical name. This problem has become critical in many applications, particularly in online bibliography systems, such as DBLP and CiterSeer. Although much work has been conducted to address this problem, there still exist many challenges. In this paper, a general framework of constraint-based topic modeling is proposed, which can make use of user-defined constraints to enhance the performance of name disambiguation. A Gibbs sampling algorithm that integrates the constraints has been proposed to do the inference of the topic model. Experimental results on a real-world dataset show that significant improvements can be obtained by taking the proposed approach.     

On-demand e-supply chain integration: A multi-agent constraint-based approach

With e-business emerging as a key enabler to drive supply chains, the focus of supply chain management has been shifted from production efficiency to customer-driven and partnership synchronization approaches. This strategic shift depends on the match between the demands and offerings that deliver the services. To achieve this, we need to coordinate the flow of information among the services, and link their business processes under various constraints. Existing approaches to this problem have relied on complete information of services and resources, and have failed to adequately address the dynamics and uncertainties of the operating environments. The real-world situation is complicated as a result of undetermined requirements of services involved in the chain, unpredictable solutions contributed by service providers, and dynamic selection and aggregation of solutions to services. This paper examines an agent-mediated approach to on-demand e-business supply chain integration. Each agent works as a service broker, exploring individual service decisions as well as interacting with each other for achieving compatibility and coherence among the decisions of all services. Based on the framework, a prototype has been implemented with simulated experiments highlighting the effectiveness of the approach.     

A component-based approach to telecommunication software

This article discusses a domain-specific component-based architecture called distributed feature composition. Based on the well-known pipe-and-filter architecture, DFC has been refined for use in the telecommunication domain. The authors also point to ways this approach might work in other domains, including data networks     

A practical approach to software metrics

Metrics programs that create meaningful change in software practice must start with business goals in mind. Software metrics are quantitative standards of measurement for various aspects of software projects. A well-designed metrics program will support decision making by management and enhance return on the IT investment. There are many aspects of software projects that can be measured, but not all aspects are worth measuring. Starting a new metrics program or improving a current program consists of five steps: identify business goals; select metrics; gather historical data; automate measurement procedures; and use metrics in decision making     

A case-based approach to software reuse

This software reuse system helps a user build programs by reusing modules stored in an existing library. The system, dubbed caesar (Case-basEd SoftwAre Reuse), is conceived in the case-based reasoning framework, where cases consist of program specifications and the corresponding C language code. The case base is initially seeded by decomposing relevant programs into functional slices using algorithms from dataflow analysis. caesar retrieves stored specifications from this base and specializes and/or generalizes them to match the user specification. Testing techniques are applied to the construct assembled by caesar through sequential composition to generate test data which exhibits the behavior of the code. For efficiency, inductive logic programming techniques are used to capture combinations of functions that frequently occur together in specifications. Such combinations may be stored as new functional slices.     

A Bayesian network approach to assess and predict software quality using activity-based quality models

ContextSoftware quality is a complex concept. Therefore, assessing and predicting it is still challenging in practice as well as in research. Activity-based quality models break down this complex concept into concrete definitions, more precisely facts about the system, process, and environment as well as their impact on activities performed on and with the system. However, these models lack an operationalisation that would allow them to be used in assessment and prediction of quality. Bayesian networks have been shown to be a viable means for this task incorporating variables with uncertainty.ObjectiveThe qualitative knowledge contained in activity-based quality models are an abundant basis for building Bayesian networks for quality assessment. This paper describes a four-step approach for deriving systematically a Bayesian network from an assessment goal and a quality model.MethodThe four steps of the approach are explained in detail and with running examples. Furthermore, an initial evaluation is performed, in which data from NASA projects and an open source system is obtained. The approach is applied to this data and its applicability is analysed.ResultsThe approach is applicable to the data from the NASA projects and the open source system. However, the predictive results vary depending on the availability and quality of the data, especially the underlying general distributions.ConclusionThe approach is viable in a realistic context but needs further investigation in case studies in order to analyse its predictive validity.     

A constraint-based approach for human–robot collision avoidance

In this paper, we present a software-based approach for collision avoidance that can be applied in human–robot collaboration scenarios. One of the contributions is a method for converting clustered 3D sensor data into computationally efficient convex hull representations used for robot-obstacle distance computation. Based on the computed distance vectors, we generate collision avoidance motions using a potential field approach and integrate them with other simultaneously running robot tasks in a constraint-based control framework. In order to improve control performance, we apply evolutionary techniques for parameter optimization within this framework based on selected quality criteria. Experiments are performed on a dual-arm robotic system equipped with several depth cameras. The approach is able to generate task-compliant avoidance motions in dynamic environments with high performance.     

A state-based approach to the representation and recognition ofgesture

A state-based technique for the representation and recognition of gesture is presented. We define a gesture to be a sequence of states in a measurement or configuration space. For a given gesture, these states are used to capture both the repeatability and variability evidenced in a training set of example trajectories. Using techniques for computing a prototype trajectory of an ensemble of trajectories, we develop methods for defining configuration states along the prototype and for recognizing gestures from an unsegmented, continuous stream of sensor data. The approach is illustrated by application to a range of gesture-related sensory data: the two-dimensional movements of a mouse input device, the movement of the hand measured by a magnetic spatial position and orientation sensor, and, lastly, the changing eigenvector projection coefficients computed from an image sequence     

A comprehensive program representation of object-oriented software

An extensible representation for object-oriented programs, the Object-oriented Program Dependence Graph (OPDG), is presented. The representation is divided into three layers: a first layer that presents the class inheritance structure, a second layer that combines a traditional control dependence subgraph and a data dependence subgraph with objects, and a third layer that presents the dynamic, runtime aspects of an object-oriented program as an Object Dependence Subgraph. The representation is modular allowing specific tools to only use the portion required for the tool's operation. The complete representation provides information sufficient for most program analysis techniques including data flow analysis, reverse engineering, interactive debuggers and other tools.     

An ensemble evolutionary constraint-based approach to understand the emergence of metabolic phenotypes

Constraint-based modeling is largely used in computational studies of metabolism. We propose here a novel approach that aims to identify ensembles of flux distributions that comply with one or more target phenotype(s). The methodology has been tested on a small-scale model of yeast energy metabolism. The target phenotypes describe the differential pattern of ethanol production and O₂ consumption observed in “Crabtree-positive” and “Crabtree-negative” yeasts in changing environment (i.e., when the upper limit of glucose uptake is varied). The ensembles were obtained either by selection among sampled flux distributions or by means of a search heuristic (genetic algorithm). The former approach provided indication about the probability to observe a given phenotype, but the resulting ensembles could not be unambiguously partitioned into “Crabtree-positive” and “Crabtree-negative” clusters. On the contrary well-separated clusters were obtained with the latter method. The cluster analysis further allowed identification of distinct groups within each target phenotype. The method may thus prove useful in characterizing the design principles underlying metabolic plasticity arising from evolving physio-pathological or developmental constraints.     

A geometric constraint-based approach to force and motion coupling between real and virtual mechanisms

This paper presents a new stable controller design for use with a general 6-d.o.f. robotic haptic device. A virtual manipulator concept is developed that couples the haptic device kinematics with the constraints of a simulated manipulator. Using impedance-based constraints, the haptic device is controlled to oppose applied forces in directions orthogonal to the motions of the virtual manipulator. The virtual manipulator kinematics constrains the haptic device motion, allowing motion to simulate specific virtual mechanisms. The implementation is illustrated using experimental simulations.     

Transformational approach to the development of software architectures on the basis of fuzzy graph models

An approach to the creation of software architectures is examined that is based on the model-oriented paradigm that opens up the way to the implementation of transformational evolution of software systems. The emphasis is on the formalization of fuzzy graphs, fuzzy graph models, and rules of their transformation. A theoretical categorical representation of fuzzy software architectures is developed, which allows one to manage the process of their transformations on the basis of making decisions in a fuzzy modeling space on the functionality of the target platform-specific system. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 5, pp. 139–150, September–October 2008.     

A comprehensive approach to optimal software rejuvenation

Software aging is caused by resource exhaustion and can lead to progressive performance degradation or result in a crash. We develop experiments that simulate an on-line bookstore application, using the standard configuration of TPC-W benchmark. We study application failures due to memory leaks, using the accelerated life testing (ALT). ALT significantly reduces the time needed to estimate the time to failure at normal level. We then select the Weibull time to failure distribution at normal level, to be used in a semi-Markov model so as to optimize the software rejuvenation trigger interval. Then we derive the optimal rejuvenation schedule interval by fixed point iteration and by an alternative non-parametric estimation algorithm. Finally, we develop a simulation model using importance sampling (IS) to cross validate the ALT experimental results and the semi-Markov model, and also we apply the non-parametric method to cross validate the optimized trigger intervals by comparing the availabilities obtained from the semi-Markov model and those from IS simulation using the non-parametric method.