On notions of regularity for data languages

With motivation from considerations in XML database theory and model checking, data strings have been introduced as an extension of finite alphabet strings which carry, at each position, a symbol and a data value from an infinite domain. Previous work has shown that it is difficult to come up with an expressive yet decidable automaton model for data languages. Recently, such a model, data automata, was introduced. This paper introduces a simpler but equivalent model and investigates its expressive power, algorithmic and closure properties, and some extensions.     

DYFRAM: dynamic fragmentation and replica management in distributed database systems

In distributed database systems, tables are frequently fragmented and replicated over a number of sites in order to reduce network communication costs. How to fragment, when to replicate and how to allocate the fragments to the sites are challenging problems that has previously been solved either by static fragmentation, replication and allocation, or based on a priori query analysis. Many emerging applications of distributed database systems generate very dynamic workloads with frequent changes in access patterns from different sites. In such contexts, continuous refragmentation and reallocation can significantly improve performance. In this paper we present DYFRAM, a decentralized approach for dynamic table fragmentation and allocation in distributed database systems based on observation of the access patterns of sites to tables. The approach performs fragmentation, replication, and reallocation based on recent access history, aiming at maximizing the number of local accesses compared to accesses from remote sites. We show through simulations and experiments on the DASCOSA distributed database system that the approach significantly reduces communication costs for typical access patterns, thus demonstrating the feasibility of our approach.     

Domain science and engineering from computer science to the sciences of informatics. Part I: Engineering

In this paper, we wish to advocate that departments of computer science put emphasis on teaching programming and software engineering based on formal methods; and more emphasis on research into formal methods for the trustworthy development of software. We also wish to advocate that the concepts of domain science and domain engineering become an indispensable part of the science of informatics and of software engineering.     

Optimal Sparse Matrix Dense Vector Multiplication in the I/O-Model

We study the problem of sparse-matrix dense-vector multiplication (SpMV) in external memory. The task of SpMV is to compute y:=Ax, where A is a sparse N×N matrix and x is a vector. We express sparsity by a parameter k, and for each choice of k consider the class of matrices where the number of nonzero entries is kN, i.e., where the average number of nonzero entries per column is k.     

Incremental Linear Discriminant Analysis Using Sufficient Spanning Sets and Its Applications

This paper presents an incremental learning solution for Linear Discriminant Analysis (LDA) and its applications to object recognition problems. We apply the sufficient spanning set approximation in three steps i.e. update for the total scatter matrix, between-class scatter matrix and the projected data matrix, which leads an online solution which closely agrees with the batch solution in accuracy while significantly reducing the computational complexity. The algorithm yields an efficient solution to incremental LDA even when the number of classes as well as the set size is large. The incremental LDA method has been also shown useful for semi-supervised online learning. Label propagation is done by integrating the incremental LDA into an EM framework. The method has been demonstrated in the task of merging large datasets which were collected during MPEG standardization for face image retrieval, face authentication using the BANCA dataset, and object categorisation using the Caltech101 dataset.     

Efficient search based on content similarity over self-organizing P2P networks

The advent of the World Wide Web has made an enormous amount of information available to everyone and the widespread use of digital equipment enables end-users (peers) to produce their own digital content. This vast amount of information requires scalable data management systems. Peer-to-peer (P2P) systems have so far been well established in several application areas, with file-sharing being the most prominent. The next challenge that needs to be addressed is (more complex) data sharing, management and query processing, thus facilitating the delivery of a wide spectrum of novel data-centric applications to the end-user, while providing high Quality-of-Service. In this paper, we propose a self-organizing P2P system that is capable to identify peers with similar content and intentionally assign them to the same super-peer. During content retrieval, fewer super-peers need to be contacted and therefore efficient similarity search is supported, in terms of reduced network traffic and contacted peers. Our approach increases the responsiveness and reliability of a P2P system and we demonstrate the advantages of our approach using large-scale simulations.     

Musculoskeletal complaints among nurses related to patient handling tasks and psychosocial factors--based on logbook registrations

The aims were to evaluate the inter-method reliability of a registration sheet for patient handling tasks, to study the day-to-day variation of musculoskeletal complaints (MSC) and to examine whether patient handling tasks and psychosocial factors were associated with MSC.Nurses (n = 148) fulfilled logbooks for three consecutive working days followed by a day off. Low back pain (LBP), neck/shoulder pain (NSP), knee pain (KP), psychosocial factors (time pressure, stress, conscience of the quality of work) and patient transfers and care tasks were reported.The logbook was reliable for both transfer and care tasks. The numbers of nurses reporting MSC and the level of pain increased significantly during the three working days (15%-30% and 17%-37%, respectively) and decreased on the day off. Stress and transfer task were associated with LPB and transfer tasks were associated with KP.Our results confirm a relationship between work factors and MSC and indicate that logs could be one way to obtain a better understanding of the complex interaction of various nursing working conditions in relation to MSC.     

Response of structures to planar blast loads – A finite element engineering approach

Design and validation of structures against blast loads are important for modern society in order to protect and secure its citizen. Since it is a challenge to validate and optimise protective structures against blast loads using full-scale experimental tests, we have to turn our attention towards advanced numerical tools like the finite element method. Several different finite element techniques can be used to describe the response of structures due to blast loads. Some of these are: (1) a pure Lagrangian formulation, (2) an initial Eulerian simulation (to determine the load) followed by a Lagrangian simulation (for the structural response) and (3) a hybrid technique that combines the advantages of Eulerian and Lagrangian methods to have a full coupling between the blast waves and the deformation of the structure. Ideally, all blast simulations should be carried out using the fully coupled Eulerian–Lagrangian approach, but this may not be practical as the computational time increases considerably when going from a pure Lagrangian to a fully coupled Eulerian–Lagrangian simulation. A major goal in this study is to investigate if a pure Lagrangian formulation can be applied to determine the structural response in a specified blast load problem or if more advanced approaches such as the fully coupled Eulerian–Lagrangian approach is required for reliable results. This is done by conducting numerical simulations of an unprotected 20 ft ISO container exposed to a blast load of 4000 kg TNT at 120 m standoff distance using the three different approaches presented above. To validate and discuss the results, the simulated response of the container is compared to available data from a full-scale blast test under such conditions.     

Improving convergence of Iterative Feedback Tuning

Iterative Feedback Tuning constitutes an attractive control loop tuning method for processes in the absence of an accurate process model. It is a purely data driven approach aiming at optimizing the closed loop performance. The standard formulation ensures an unbiased estimate of the loop performance cost function gradient with respect to the control parameters. This gradient is important in a search algorithm. The extension presented in this paper further ensures informative data to improve the convergence properties of the method and hence reduce the total number of required plant experiments especially when tuning for disturbance rejection. Informative data is achieved through application of an external probing signal in the tuning algorithm. The probing signal is designed based on a constrained optimization which utilizes an approximate black box model of the process. This model estimate is further used to guarantee nominal stability and to improve the parameter update using a line search algorithm for determining the iteration step size. The proposed algorithm is compared to the classical formulation in a simulation study of a disturbance rejection problem. This type of problem is notoriously difficult for Iterative Feedback Tuning due to the lack of excitation from the reference.