A global search framework for practical three-dimensional packing with variable carton orientations

This article aims to tackle a practical three-dimensional packing problem, where a number of cartons of diverse sizes are to be packed into a bin with fixed length and width but open height. Each carton is allowed to be packed in any one of the six orientations, and the carton edges are parallel to the bin edges. The allowance of variable carton orientations exponentially increases the solution space and makes the problem very challenging to solve. This study first elaborately devises the packing procedure, which converts an arbitrary sequence of cartons into a compact packing solution and subsequently develops an improved genetic algorithm (IGA) to evolve a set of solutions. Moreover, a novel global search framework (GSF), utilizing the concept of evolutionary gradient, is proposed to further improve the solution quality. Numerical experiments indicate that IGA provides faster and better results and GSF demonstrates its superior performance, especially in solving relative large-size and heterogeneous instances. Applying the proposed algorithms to some benchmarking cases of the three-dimensional strip packing problem also indicates that the algorithms are robust and effective compared to existing methods in the literature.     

Self-emergence of a common lexicon by evolution in teams of autonomous agents

In multi-agent systems, the study of language and communication is an active field of research. In this paper we present the application of evolutionary strategies to the self-emergence of a common lexicon in a population of agents. By modeling the vocabulary or lexicon of each agent as an association matrix or look-up table that maps the meanings (i.e. the objects encountered by the agents or the states of the environment itself) into symbols or signals we check whether it is possible for the population to converge in an autonomous, decentralized way to a common lexicon, so that the communication efficiency of the entire population is optimal. We have conducted several experiments aimed at testing whether it is possible to converge with evolutionary strategies to an optimal Saussurean communication system. We have organized our experiments alongside two main lines: first, we have investigated the effect of the population size on the convergence results. Second, and foremost, we have also investigated the effect of the lexicon size on the convergence results. To analyze the convergence of the population of agents we have defined the population's consensus when all the agents (i.e. 100% of the population) share the same association matrix or lexicon. As a general conclusion we have shown that evolutionary strategies are powerful enough optimizers to guarantee the convergence to lexicon consensus in a population of autonomous agents.     

Classical and superposed learning for quantum weightless neural networks

A supervised learning algorithm for quantum neural networks (QNN) based on a novel quantum neuron node implemented as a very simple quantum circuit is proposed and investigated. In contrast to the QNN published in the literature, the proposed model can perform both quantum learning and simulate the classical models. This is partly due to the neural model used elsewhere which has weights and non-linear activations functions. Here a quantum weightless neural network model is proposed as a quantisation of the classical weightless neural networks (WNN). The theoretical and practical results on WNN can be inherited by these quantum weightless neural networks (qWNN). In the quantum learning algorithm proposed here patterns of the training set are presented concurrently in superposition. This superposition-based learning algorithm (SLA) has computational cost polynomial on the number of patterns in the training set.     

A new robust algorithmic for multi-camera calibration with a 1D object under general motions without prior knowledge of any camera intrinsic parameter

In computer vision, camera calibration is a necessary process when the retrieval of information such as angles and distances is required. This paper addresses the multi-camera calibration problem with a single dimension calibration pattern under general motions. Currently, the known algorithms for solving this problem are based on the estimation of vanishing points. However, this estimate is very susceptible to noise, making the methods unsuitable for practical applications. Instead, this paper presents a new calibration algorithm, where the cameras are divided into binocular sets. The fundamental matrix of each binocular set is then estimated, allowing to perform a projective calibration of each camera. Then, the calibration is updated for the Euclidean space, ending the process. The calibration is possible without imposing any restrictions on the movement of the pattern and without any prior information about the cameras or motion. Experiments on synthetic and real images validate the new method and show that its accuracy makes it suitable also for practical applications.     

A case study on business process recovery using an e‐government system

Business processes have become one of the key assets of organization, since these processes allow them to discover and control what occurs in their environments, with information systems automating most of an organization's processes. Unfortunately, and as a result of uncontrolled maintenance, information systems age over time until it is necessary to replace them with new and modernized systems. However, while systems are aging, meaningful business knowledge that is not present in any of the organization's other assets gradually becomes embedded in them. The preservation of this knowledge through the recovery of the underlying business processes is, therefore, a critical problem. This paper provides, as a solution to the aforementioned problem, a model‐driven procedure for recovering business processes from legacy information systems. The procedure proposes a set of models at different abstraction levels, along with the model transformations between them. The paper also provides a supporting tool, which facilitates its adoption. Moreover, a real‐life case study concerning an e‐government system applies the proposed recovery procedure to validate its effectiveness and efficiency. The case study was carried out by following a formal protocol to improve its rigor and replicability. Copyright © 2011 John Wiley & Sons, Ltd.     

A warning concerning the estimation of multinomial logistic models with correlated responses in SAS

Kuss and McLerran in a paper in this journal provide SAS code for the estimation of multinomial logistic models for correlated data. Their motivation derived from two papers that recommended to estimate such models using a Poisson likelihood, which is according to Kuss and McLerran "statistically correct but computationally inefficient". Kuss and McLerran propose several estimating methods. Some of these are based on the fact that the multinomial model is a multivariate binary model. Subsequently a procedure proposed by Wright is exploited to fit the models. In this paper we will show that the new computation methods, based on the approach by Wright, are statistically incorrect because they do not take into account that for multinomial data a multivariate link function is needed. An alternative estimation strategy is proposed using the clustered bootstrap.     

Verifying compiled file system code

This article presents a case study on retrospective verification of the Linux Virtual File System (VFS), which is aimed at checking violations of API usage rules and memory properties. Since VFS maintains dynamic data structures and is written in a mixture of C and inlined assembly, modern software model checkers cannot be applied. Our case study centres around our novel automated software verification tool, the SOCA Verifier, which symbolically executes and analyses compiled code. We describe how this verifier deals with complex features such as memory access, pointer aliasing and computed jumps in the VFS implementation, while reducing manual modelling to a minimum. Our results show that the SOCA Verifier is capable of analysing the complex Linux VFS implementation reliably and efficiently, thereby going beyond traditional testing tools and into niches that current software model checkers do not reach. This testifies to the SOCA Verifier’s suitability as an effective and efficient bug-finding tool during the development of operating system components.     

Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking

This paper deals with the problem of supervised wrapper-based feature subset selection in datasets with a very large number of attributes. Recently the literature has contained numerous references to the use of hybrid selection algorithms: based on a filter ranking, they perform an incremental wrapper selection over that ranking. Though working fine, these methods still have their problems: (1) depending on the complexity of the wrapper search method, the number of wrapper evaluations can still be too large; and (2) they rely on a univariate ranking that does not take into account interaction between the variables already included in the selected subset and the remaining ones.Here we propose a new approach whose main goal is to drastically reduce the number of wrapper evaluations while maintaining good performance (e.g. accuracy and size of the obtained subset). To do this we propose an algorithm that iteratively alternates between filter ranking construction and wrapper feature subset selection (FSS). Thus, the FSS only uses the first block of ranked attributes and the ranking method uses the current selected subset in order to build a new ranking where this knowledge is considered. The algorithm terminates when no new attribute is selected in the last call to the FSS algorithm. The main advantage of this approach is that only a few blocks of variables are analyzed, and so the number of wrapper evaluations decreases drastically.The proposed method is tested over eleven high-dimensional datasets (2400-46,000 variables) using different classifiers. The results show an impressive reduction in the number of wrapper evaluations without degrading the quality of the obtained subset.