Neural Network Based Model Refinement

In this paper, model bases and model generators are presented in the context of model refinement. This article proposes a neural network based model refinement technique for software metrics estimation. Neural networks are introduced as instruments of model refinement. A refinement technique is proposed for ranking and selecting input variables. A case study shows the practice of model refinement using neural networks.model refinement, neural network, software metrics, Model generators

Extensible Component Based Architecture for FLASH, A Massively Parallel, Multiphysics Simulation Code

FLASH is a publicly available high performance application code which has evolved into a modular, extensible software system from a collection of unconnected legacy codes. FLASH has been successful because its capabilities have been driven by the needs of scientific applications, without compromising maintainability, performance, and usability. In its newest incarnation, FLASH3 consists of inter-operable modules that can be combined to generate different applications. The FLASH architecture allows arbitrarily many alternative implementations of its components to co-exist and interchange with each other, resulting in greater flexibility. Further, a simple and elegant mechanism exists for customization of code functionality without the need to modify the core implementation of the source. A built-in unit test framework providing verifiability, combined with a rigorous software maintenance process, allow the code to operate simultaneously in the dual mode of production and development. In this paper we describe the FLASH3 architecture, with emphasis on solutions to the more challenging conflicts arising from solver complexity, portable performance requirements, and legacy codes. We also include results from user surveys conducted in 2005 and 2007, which highlight the success of the code.Comment: 33 pages, 7 figures; revised paper submitted to Parallel Computin

An Evolutionary Learning Approach for Adaptive Negotiation Agents

Developing effective and efficient negotiation mechanisms for real-world applications such as e-Business is challenging since negotiations in such a context are characterised by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This paper illustrates our adaptive negotiation agents which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real-world applications

Afivo: a framework for quadtree/octree AMR with shared-memory parallelization and geometric multigrid methods

Afivo is a framework for simulations with adaptive mesh refinement (AMR) on quadtree (2D) and octree (3D) grids. The framework comes with a geometric multigrid solver, shared-memory (OpenMP) parallelism and it supports output in Silo and VTK file formats. Afivo can be used to efficiently simulate AMR problems with up to about $10^{8}$ unknowns on desktops, workstations or single compute nodes. For larger problems, existing distributed-memory frameworks are better suited. The framework has no built-in functionality for specific physics applications, so users have to implement their own numerical methods. The included multigrid solver can be used to efficiently solve elliptic partial differential equations such as Poisson's equation. Afivo's design was kept simple, which in combination with the shared-memory parallelism facilitates modification and experimentation with AMR algorithms. The framework was already used to perform 3D simulations of streamer discharges, which required tens of millions of cells

Towards a Framework for Managing Inconsistencies in Systems of Systems

The growth in the complexity of software systems has led to a proliferation of systems that have been created independently to provide specific functions, such as activity tracking, household energy management or personal nutrition assistance. The runtime composition of these individual systems into Systems of Systems (SoSs) enables support for more sophisticated functionality that cannot be provided by individual constituent systems on their own. However, in order to realize the benefits of these functionalities it is necessary to address a number of challenges associated with SoSs, including, but not limited to, operational and managerial independence, geographic distribution of participating systems, evolutionary development, and emergent conflicting behavior that can occur due interactions between the requirements of the participating systems. In this paper, we present a framework for conflict management in SoSs. The management of conflicting requirements involves four steps, namely (a) overlap detection, (b) conflict identification, (c) conflict diagnosis, and (d) conflict resolution based on the use of a utility function. The framework uses a Monitor-Analyze-Plan- Execute- Knowledge (MAPE-K) architectural pattern. In order to illustrate the work, we use an example SoS ecosystem designed to support food security at different levels of granularity

Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

Initiating technical refinements in high-level golfers: Evidence for contradictory procedures

When developing motor skills there are several outcomes available to an athlete depending on their skill status and needs. Whereas the skill acquisition and performance literature is abundant, an under-researched outcome relates to the refinement of already acquired and well-established skills. Contrary to current recommendations for athletes to employ an external focus of attention and a representative practice design, Carson and Collins’ (2011) [Refining and regaining skills in fixation/diversification stage performers: The Five-A Model. International Review of Sport and Exercise Psychology, 4, 146–167. doi:10.1080/1750984x.2011.613682] Five-A Model requires an initial narrowed internal focus on the technical aspect needing refinement: the implication being that environments which limit external sources of information would be beneficial to achieving this task. Therefore, the purpose of this paper was to (1) provide a literature-based explanation for why techniques counter to current recommendations may be (temporarily) appropriate within the skill refinement process and (2) provide empirical evidence for such efficacy. Kinematic data and self-perception reports are provided from high level golfers attempting to consciously initiate technical refinements while executing shots onto a driving range and into a close proximity net (i.e. with limited knowledge of results). It was hypothesised that greater control over intended refinements would occur when environmental stimuli were reduced in the most unrepresentative practice condition (i.e. hitting into a net). Results confirmed this, as evidenced by reduced intra-individual movement variability for all participants’ individual refinements, despite little or no difference in mental effort reported. This research offers coaches guidance when working with performers who may find conscious recall difficult during the skill refinement process