Implementing global constraints as graphs of elementary constraints

Global constraints are cardinal concepts of CLP (FD), a constraint programming language. They are means to find a set of integers that satisfy certain relations. The fact that defining global constraints often requires the knowledge of a specification language makes sharing constraints between scientists and programmers difficult. Nicolas Beldiceanu presented a theory that could solve this problem, because it depicts global constraints as graphs: an abstraction that everyone understands. The abstract description language defined by the theory may also be interpreted by a computer program. This paper deals with the problematic issues of putting the theory into practice by implementing such a program. It introduces a concrete syntax of the language and presents three programs understanding that syntax. These case studies represent two different approaches of propagation. One of these offers exhausting pruning with poor efficiency, the other, yet unfinished attempt provides a better alternative at the cost of being a lot more complicated

Synthesising robust schedules for minimum disruption repair using linear programming

An off-line scheduling algorithm considers resource, precedence, and synchronisation requirements of a task graph, and generates a schedule guaranteeing its timing requirements. This schedule must, however, be executed in a dynamic and unpredictable operating environment where resources may fail and tasks may execute longer than expected. To accommodate such execution uncertainties, this paper addresses the synthesis of robust task schedules using a slack-based approach and proposes a solution using integer linear programming (ILP). Earlier we formulated a time slot based ILP model whose solutions maximise the temporal flexibility of the overall task schedule. In this paper, we propose an improved, interval based model, compare it to the former, and evaluate both on a set of random scenarios using two public domain ILP solvers and a proprietary SAT/ILP mixed solver

Feature space reduction method for ultrahigh-dimensional, multiclass data: Random forest-based multiround screening (RFMS)

In recent years, several screening methods have been published for ultrahigh-dimensional data that contain hundreds of thousands of features, many of which are irrelevant or redundant. However, most of these methods cannot handle data with thousands of classes. Prediction models built to authenticate users based on multichannel biometric data result in this type of problem. In this study, we present a novel method known as random forest-based multiround screening (RFMS) that can be effectively applied under such circumstances. The proposed algorithm divides the feature space into small subsets and executes a series of partial model builds. These partial models are used to implement tournament-based sorting and the selection of features based on their importance. This algorithm successfully filters irrelevant features and also discovers binary and higher-order feature interactions. To benchmark RFMS, a synthetic biometric feature space generator known as BiometricBlender is employed. Based on the results, the RFMS is on par with industry-standard feature screening methods, while simultaneously possessing many advantages over them

A prototype home robot with an ambient facial interface to improve drug compliance

We have developed a prototype home robot to improve drug compliance. The robot is a small mobile device, capable of autonomous behaviour, as well as remotely controlled operation via a wireless datalink. The robot is capable of face detection and also has a display screen to provide facial feedback to help motivate patients and thus increase their level of compliance. An RFID reader can identify tags attached to different objects, such as bottles, for fluid intake monitoring. A tablet dispenser allows drug compliance monitoring. Despite some limitations, experience with the prototype suggests that simple and low-cost robots may soon become feasible for care of people living alone or in isolation