Screening is a low complexity halftoning algorithm that has been widely used in many applications. However, screen design requires that the stacking property be obeyed. This constraint limits the texture quality at each gray level. We present a look-up-table based halftoning algorithm for which the stacking constraint is not necessarily satisfied; but the binary patterns for individual levels a...

The management of semantical quality is a major challenge in the context of knowledge integration. In this paper, we describe a new approach to constraint management that emphasizes constraint traceability when moving from the semantical level to the operational one. Our strategy for management of semantical quality is related to a metamodeling-based approach to knowledge integration. We carry ...

Architectural styles and patterns have been studied since the inception of software architecture as a discipline. We generalise architectural styles, patterns and similar concepts by introducing the notion of architectural constraints. An architectural constraint is a vehicle for the reuse of architectural design knowledge and for the improvement of software quality. It may be used for improvin...

In this paper, we present a case study that illustrates the utility of pattern tableau discovery for data quality analysis. Given a usersupplied integrity constraint, such as a boolean predicate expected to be satisfied by every tuple, a functional dependency, or an inclusion dependency, a pattern tableau is a concise summary of subsets of the data that satisfy or fail the constraint. We descri...

Service-oriented architecture is an architectural approach that can be applied for building autonomous service systems dynamically to satisfy on-demand business requests. During the execution of service compositions, architectural constraint violations relating to functional and non-fucntional system properties need to be handled intelligently and autonomously, possibly requiring architectural ...

We study the problem of predicting a set or list of options under knapsack constraint. The quality of such lists are evaluated by a submodular reward function that measures both quality and diversity. Similar to DAgger (Ross et al., 2010), by a reduction to online learning, we show how to adapt two sequence prediction models to imitate greedy maximization under knapsack constraint problems: CON...

Next generations of image and video coding methods should of course be efficient in terms of compression, but also propose advanced functionalities. Among these functionalities such as scalability, lossy and lossless coding, data protection, Rate Distortion Optimization (RDO) and Rate Control (RC) are key issues. RDO aims at optimizing compression performances, while RC mechanism enables to exa...

Distributed Constraint Optimization (DCOP) is a popular framework for cooperative multi-agent decision making. DCOP is NPhard, so an important line of work focuses on developing fast incomplete solution algorithms for large-scale applications. One of the few incomplete algorithms to provide bounds on solution quality is k-size optimality, which defines a local optimality criterion based on the ...

The Distributed Constraint Optimization Problem (DCOP) is a promising approach for modeling distributed reasoning tasks that arise in multiagent systems. Unfortunately, existing methods for DCOP are not able to provide theoretical guarantees on global solution quality while allowing agents to operate asynchronously. We show how this failure can be remedied by allowing agents to make local decis...

Some distributed constraint optimization algorithms use a linear number of messages in the number of agents, but of exponential size. This is often the main limitation for their practical applicability. Here we present some distributed algorithms for these problems when they are arranged in a tree of agents. The exact algorithm, DCTE, computes the optimal solution but requires messages of size ...