Quasi-Monte Carlo methods for Markov chains with continuous multi-dimensional state space

We describe a quasi-Monte Carlo method for the simulation of discrete time Markov chains with continuous multi-dimensional state space. The method simulates copies of the chain in parallel. At each step the copies are reordered according to their successive coordinates. We prove the convergence of the method when the number of copies increases. We illustrate the method with numerical examples where the simulation accuracy is improved by large factors compared with Monte Carlo simulation.     

Editors’ note

Editorial Note

Editors’ note     

Haar invariant signatures and spatial recognition using omnidirectional visual information only

This paper describes a method for spatial representation, place recognition and qualitative self-localization in dynamic indoor environments, based on omnidirectional images. This is a difficult problem because of the perceptual ambiguity of the acquired images, and their weak robustness to noise, geometrical and photometric variations of real world scenes. The spatial representation is built up invariant signatures using Invariance Theory where we suggest to adapt Haar invariant integrals to the particular geometry and image transformations of catadioptric omnidirectional sensors. It follows that combining simple image features in a process of integration over visual transformations and robot motion, can build discriminant percepts about robot spatial locations. We further analyze the invariance properties of the signatures and the apparent relation between their similarity measures and metric distances. The invariance properties of the signatures can be adapted to infer a hierarchical process, from global room recognition to local and coarse robot localization.     

Collaborative user modeling with user-generated tags for social recommender systems

With the popularity of social media services, the sheer amount of content is increasing exponentially on the Social Web that leads to attract considerable attention to recommender systems. Recommender systems provide users with recommendations of items suited to their needs. To provide proper recommendations to users, recommender systems require an accurate user model that can reflect a user’s characteristics, preferences and needs. In this study, by leveraging user-generated tags as preference indicators, we propose a new collaborative approach to user modeling that can be exploited to recommender systems. Our approach first discovers relevant and irrelevant topics for users, and then enriches an individual user model with collaboration from other similar users. In order to evaluate the performance of our model, we compare experimental results with a user model based on collaborative filtering approaches and a vector space model. The experimental results have shown the proposed model provides a better representation in user interests and achieves better recommendation results in terms of accuracy and ranking.     

Disassembly Sequencing Using Tabu Search

End-of-life disassembly has developed into a major research area within the sustainability paradigm, resulting in the emergence of several algorithms and structures proposing heuristics techniques such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Neural Networks (NN). The performance of the proposed methodologies heavily depends on the accuracy and the flexibility of the algorithms to accommodate several factors such as preserving the precedence relationships during disassembly while obtaining near- optimal and optimal solutions. This paper improves a previously proposed Genetic Algorithm model for disassembly sequencing by utilizing a faster metaheuristic algorithm, Tabu search, to obtain the optimal solution. The objectives of the proposed algorithm are to minimize (1) the traveled distance by the robotic arm, (2) the number of disassembly method changes, and (3) the number of robotic arm travels by combining the identical-material components together and hence eliminating unnecessary disassembly operations. In addition to improving the quality of optimum sequence generation, a comprehensive statistical analysis comparing the previous Genetic Algorithm and the proposed Tabu Search Algorithm is also included     

Symbolic abstraction and deadlock-freeness verification of inter-enterprise processes

The design of complex inter-enterprise business processes (IEBP) is generally performed in a modular way. Each process is designed separately and then the whole IEBP is obtained by composition. Even if such a modular approach is intuitive and facilitates the design problem, it poses the problem that correct behavior of each business process of the IEBP taken alone does not guarantee a correct behavior of the composed IEBP (i.e. properties are not preserved by composition). Proving correctness of the (unknown) composed process is strongly related to the model checking problem of a system model. Among others, the symbolic observation graph based approach has proven to be very helpful for efficient model checking in general. Since it is heavily based on abstraction techniques and thus hides detailed information about system components that are not relevant for the correctness decision, it is promising to transfer this concept to the problem raised in this paper: How can the symbolic observation graph technique be adapted and employed for process composition? Answering this question is the aim of this paper.     

A two-stage gene selection scheme utilizing MRMR filter and GA wrapper

Gene expression data usually contain a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best discriminates biological samples of different types. In this paper, we propose a two-stage selection algorithm for genomic data by combining MRMR (Minimum Redundancy–Maximum Relevance) and GA (Genetic Algorithm). In the first stage, MRMR is used to filter noisy and redundant genes in high-dimensional microarray data. In the second stage, the GA uses the classifier accuracy as a fitness function to select the highly discriminating genes. The proposed method is tested for tumor classification on five open datasets: NCI, Lymphoma, Lung, Leukemia and Colon using Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers. The comparison of the MRMR-GA with MRMR filter and GA wrapper shows that our method is able to find the smallest gene subset that gives the most classification accuracy in leave-one-out cross-validation (LOOCV).     

Validating the ISO/IEC 15504 measure of software requirementsanalysis process capability

ISO/IEC 15504 is an emerging international standard on software process assessment. It defines a number of software engineering processes and a scale for measuring their capability. One of the defined processes is software requirements analysis (SRA). A basic premise of the measurement scale is that higher process capability is associated with better project performance (i.e., predictive validity). The paper describes an empirical study that evaluates the predictive validity of SRA process capability. Assessments using ISO/IEC 15504 were conducted on 56 projects world-wide over a period of two years. Performance measures on each project were also collected using questionnaires, such as the ability to meet budget commitments and staff productivity. The results provide strong evidence of predictive validity for the SRA process capability measure used in ISO/IEC 15504, but only for organizations with more than 50 IT staff. Specifically, a strong relationship was found between the implementation of requirements analysis practices as defined in ISO/IEC 15504 and the productivity of software projects. For smaller organizations, evidence of predictive validity was rather weak. This can be interpreted in a number of different ways: that the measure of capability is not suitable for small organizations or that the SRA process capability has less effect on project performance for small organizations     

Neuro-fuzzy based time-delay estimation using DCT coefficients

In this research, a neuro-fuzzy system (NFS) is introduced into the problem of time-delay estimation. Time-delay estimation deals with the problem of estimating a constant time delay embedded within a received noisy and delayed replica of a known reference signal. The received signal is filtered and discrete cosine transformed into DCT coefficients. The time delay is now encoded into the DCT coefficients. Only those few DCT coefficients which possess high sensitivity to time-delay variations are used as input to the NFS. The NFS is used for time-delay estimation because of its ability of learning highly nonlinear relationships and encoding them into its internal structure. This capability is used in learning the nonlinear relationship between the DCT coefficients of a delayed reference signal and the time delay embedded into this signal. The NFS is trained with several hundred training sets in which the highly sensitive DCT coefficients were applied as input, and the corresponding time delay was the output. In the testing phase, DCT coefficients of delayed signals were applied as input to the NFS and the system produced accurate time-delay estimates, as compared to those obtained by the classical cross-correlation technique.