From Quantity to Quality: Massive Molecular Dynamics Simulation of Nanostructures under Plastic Deformation in Desktop and Service Grid Distributed Computing Infrastructure

The distributed computing infrastructure (DCI) on the basis of BOINC and EDGeS-bridge technologies for high-performance distributed computing is used for porting the sequential molecular dynamics (MD) application to its parallel version for DCI with Desktop Grids (DGs) and Service Grids (SGs). The actual metrics of the working DG-SG DCI were measured, and the normal distribution of host performances, and signs of log-normal distributions of other characteristics (CPUs, RAM, and HDD per host) were found. The practical feasibility and high efficiency of the MD simulations on the basis of DG-SG DCI were demonstrated during the experiment with the massive MD simulations for the large quantity of aluminum nanocrystals ($\sim10^2$-$10^3$). Statistical analysis (Kolmogorov-Smirnov test, moment analysis, and bootstrapping analysis) of the defect density distribution over the ensemble of nanocrystals had shown that change of plastic deformation mode is followed by the qualitative change of defect density distribution type over ensemble of nanocrystals. Some limitations (fluctuating performance, unpredictable availability of resources, etc.) of the typical DG-SG DCI were outlined, and some advantages (high efficiency, high speedup, and low cost) were demonstrated. Deploying on DG DCI allows to get new scientific $\it{quality}$ from the simulated $\it{quantity}$ of numerous configurations by harnessing sufficient computational power to undertake MD simulations in a wider range of physical parameters (configurations) in a much shorter timeframe.Comment: 13 pages, 11 pages (http://journals.agh.edu.pl/csci/article/view/106

Comparison and Adaptation of Automatic Evaluation Metrics for Quality Assessment of Re-Speaking

Re-speaking is a mechanism for obtaining high quality subtitles for use in live broadcast and other public events. Because it relies on humans performing the actual re-speaking, the task of estimating the quality of the results is non-trivial. Most organisations rely on humans to perform the actual quality assessment, but purely automatic methods have been developed for other similar problems, like Machine Translation. This paper will try to compare several of these methods: BLEU, EBLEU, NIST, METEOR, METEOR-PL, TER and RIBES. These will then be matched to the human-derived NER metric, commonly used in re-speaking.Comment: Comparison and Adaptation of Automatic Evaluation Metrics for Quality Assessment of Re-Speaking. arXiv admin note: text overlap with arXiv:1509.0908

Noisy-parallel and comparable corpora filtering methodology for the extraction of bi-lingual equivalent data at sentence level

Text alignment and text quality are critical to the accuracy of Machine Translation (MT) systems, some NLP tools, and any other text processing tasks requiring bilingual data. This research proposes a language independent bi-sentence filtering approach based on Polish (not a position-sensitive language) to English experiments. This cleaning approach was developed on the TED Talks corpus and also initially tested on the Wikipedia comparable corpus, but it can be used for any text domain or language pair. The proposed approach implements various heuristics for sentence comparison. Some of them leverage synonyms and semantic and structural analysis of text as additional information. Minimization of data loss was ensured. An improvement in MT system score with text processed using the tool is discussed.Comment: arXiv admin note: text overlap with arXiv:1509.09093, arXiv:1509.0888

Monte Carlo Fitting Of Data From Muon Catalyzed Fusion Experiments In Solid Hydrogen

Applying the classical chi-square fitting procedure for multiparameter systems is in somecases extremely difficult due to the lack of an analytical expression for the theoretical functionsdescribing the system. This paper presents an analysis procedure for experimental datausing theoretical functions generated by Monte Carlo method, each corresponding to definitevalues of the minimization parameters. It was applied for the E742 experiment (TRIUMF,Vancouver, Canada) data analysis with the aim to analyze data from Muon Catalyzed Fusionexperiments (extraction muonic atom scattering parameters and parameters of pd fusion inpdμ molecule)

Benchmarking High Performance Architectures With Natural Language Processing Algorithms

Natural Language Processing algorithms are resource demanding, especially when tuning toinflective language like Polish is needed. The paper presents time and memory requirementsof part of speech tagging and clustering algorithms applied to two corpora of the Polishlanguage. The algorithms are benchmarked on three high performance platforms of differentarchitectures. Additionally sequential versions and OpenMP implementations of clusteringalgorithms were compared

Deoxidation Impact on Non-Metallic Inclusions and Characterization Methods

Deoxidation is an unavoidable step in the elaboration of steel. The study of its influence could improve the quality of low-carbon steel (0.20–0.25 wt.% of carbon). There are many deoxidation methods, and the most-common one consists of adding aluminum. Although it is a classic method, determining the optimal process parameters (quantity, yield, etc.…) could be very sensitive. Deoxidation plays a determining role on inclusion cleanliness, especially on sulfide morphology. In order to control the efficiency of deoxidation, different techniques can be used. In this paper, an automated counting procedure on a scanning electron microscope with a field emission gun (FEG-SEM) is presented. This method was applied on samples cast in our laboratory under different deoxidation conditions. According to this, the resulting inclusion population is correlated with the aluminum content to find the optimal process parameters

Upnp-Based Discovery And Management Of Hypervisors And Virtual Machines

The paper introduces a Universal Plug and Play based discovery and management toolkitthat facilitates collaboration between cloud infrastructure providers and users. The presentedtools construct a unified hierarchy of devices and their management-related services, thatrepresents the current deployment of users’ (virtual) infrastructures in the provider’s (physical)infrastructure as well as the management interfaces of respective devices. The hierarchycan be used to enhance the capabilities of the provider’s infrastructure management system.To maintain user independence, the set of management operations exposed by a particulardevice is always defined by the device owner (either the provider or user)

Recovery And Migration Of Application Logic From Legacy Systems

Future Internet technologies necessitate dramatic changes in system design, deliveryand usage patterns. For many legacy applications it means that their furtherdevelopment and transition to the Internet becomes problematic or evenimpossible due to the obsolescence of technologies they use. Replacement ofthe old system with the new one, built from scratch, is usually economicallyunacceptable. Therefore, there is a call for methods and tools supportingthe automated migration of legacy systems into a new paradigm. This paperproposes a tool supported method for recovery and migration of applicationlogic information from legacy systems. The information extracted from a legacyapplication is stored in the form of precise requirement-level models enablingautomated transformation into a new system structure in a model-driven way.Evaluation of the approach is based on a case study legacy system

Enhancements Of Fuzzy Q-Learning Algorithm

Fuzzy Q-Learning algorithm combines reinforcement learning techniques with fuzzy modelling. It provides a flexible solution for automatic discovery of rules for fuzzy systems inthe process of reinforcement learning. In this paper we propose several enhancements tothe original algorithm to make it more performant and more suitable for problems withcontinuous-input continuous-output space. Presented improvements involve generalizationof the set of possible rule conclusions. The aim is not only to automatically discover anappropriate rule-conclusions assignment, but also to automatically define the actual conclusions set given the all possible rules conclusions. To improve algorithm performance whendealing with environments with inertness, a special rule selection policy is proposed

A Toolkit For Storage Qos Provisioning For Data-Intensive Applications

This paper describes a programming toolkit developed in the PL-Grid project, named QStorMan, which supports storage QoS provisioning for data-intensive applications in distributed environments. QStorMan exploits knowledge-oriented methods for matching storage resources to non-functional requirements, which are defined for a data-intensive application. In order to support various usage scenarios, QStorMan provides two interfaces, such as programming libraries or a web portal. The interfaces allow to define the requirements either directly in an application source code or by using an intuitive graphical interface. The first way provides finer granularity, e.g., each portion of data processed by an application can define a different set of requirements. The second method is aimed at legacy applications support, which source code can not be modified. The toolkit has been evaluated using synthetic benchmarks and the production infrastructure of PL-Grid, in particular its storage infrastructure, which utilizes the Lustre file system