Binding and Normalization of Binary Sparse Distributed Representations by Context-Dependent Thinning

Distributed representations were often criticized as inappropriate for encoding of data with a complex structure. However Plate's Holographic Reduced Representations and Kanerva's Binary Spatter Codes are recent schemes that allow on-the-fly encoding of nested compositional structures by real-valued or dense binary vectors of fixed dimensionality. In this paper we consider procedures of the Context-Dependent Thinning which were developed for representation of complex hierarchical items in the architecture of Associative-Projective Neural Networks. These procedures provide binding of items represented by sparse binary codevectors (with low probability of 1s). Such an encoding is biologically plausible and allows a high storage capacity of distributed associative memory where the codevectors may be stored. In contrast to known binding procedures, Context-Dependent Thinning preserves the same low density (or sparseness) of the bound codevector for varied number of component codevectors. Besides, a bound codevector is not only similar to another one with similar component codevectors (as in other schemes), but it is also similar to the component codevectors themselves. This allows the similarity of structures to be estimated just by the overlap of their codevectors, without retrieval of the component codevectors. This also allows an easy retrieval of the component codevectors. Examples of algorithmic and neural-network implementations of the thinning procedures are considered. We also present representation examples for various types of nested structured data (propositions using role-filler and predicate-arguments representation schemes, trees, directed acyclic graphs) using sparse codevectors of fixed dimension. Such representations may provide a fruitful alternative to the symbolic representations of traditional AI, as well as to the localist and microfeature-based connectionist representations

A Utility-Based Reputation Model for Grid Resource Management System

In this paper we propose extensions to the existing utility-based reputation model for virtual organizations (VOs) in grids, and present a novel approach for integrating reputation into grid resource management system. The proposed extensions include: incorporation of statistical model of user behaviour (SMUB) to assess user reputation; a new approach for assigning initial reputation to a new entity in a VO; capturing alliance between consumer and resource; time decay and score functions. The addition of the SMUB model provides robustness and dynamics to the user reputation model comparing to the policy-based user reputation model in terms of adapting to user actions. We consider a problem of integrating reputation into grid scheduler as a multi-criteria optimization problem. A non-linear trade-off scheme is applied to form a composition of partial criteria to provide a single objective function. The advantage of using such a scheme is that it provides a Pareto-optimal solution partially satisfying criteria with corresponding weights. Experiments were run to evaluate performance of the model in terms of resource management using data collected within the EGEE Grid-Observatory project. Results of simulations showed that on average a 45 % gain in performance can be achieved when using a reputation-based resource scheduling algorithm

Utility-based reputation model for VO in GRIDs

In this paper we extend the existing utility-based reputation model for VOs in Grids by incorporating a statistical model of user behaviour (SMUB) that was previously developed for computer networks and distributed systems, and different functions to address threats scenarios in the area of trust and reputation management. These modifications include: assigning initial reputation to a new entity in VO, capturing alliance between consumer and resource, time decay function, and score function.В данной статье предложена модификация существующей модели репутаций для виртуальных организаций в Grid-системах, которая основана на оценке функции полезности. Модификация модели состоит в добавлении статистической модели поведения пользователя, которая ранее была разработана для компьютерных сетей и распределенных систем, а также компонентов, которые позволяют противостоять угрозам в области управления доверием и репутацией. К числу этих компонентов относятся: механизм присвоения начальной репутации для новых субъектов виртуальной организации; учет взаимосвязей между пользователями и ресурсами; функция учета времени; а также классификация предоставляемых сервисов в Grid-системе

Development and analysis of computer vision system for micromechanics

Summary: In micromechanics the best technologies are MicroElectroMechanical Systems (MEMS) and MicroEquipment Technology (MET). The MEMS used the electronic technology to produce mechanical components. Due to the advantages of the MET such as the development of low-cost micro devices, the possibility of using various manufacturing materials, the possibility of producing three-dimensional microcomponents it will be very useful to automatize all processes of mechanics production and develop different technological innovations. The automation and robotics are two closely related technologies since automation can be defined as a technology that is related to the use of mechanical-electrical systems based on computers for the operation and control of production. The field of micromechanics has been involved in different applications that cover almost all areas of science and technology, an example of this is the management of microdevices for the autofocus of digital cameras whose objective is image processing (recognizing and locate objects). The use of computer vision systems can help to automate the work of MEMS and MET systems, so the study of image processing using a computer is very important. The objective was to design a computer vision system that allows the movement of the lens to focus the work area, for the monitoring of the micromachine tool in manufacturing processes and assembly of microcomponents in real time using previously developed image recognition algorithms. The developed algorithms use the criterion of improving the contrast of the input image. We describe our approach and obtained results. This approach can be used not only in micromechanics but in nanomechanics to

Intelligent Computations for Flood Monitoring

Floods represent the most devastating natural hazards in the world, affecting more people and causing more property damage than any other natural phenomena. One of the important problems associated with flood monitoring is flood extent extraction from satellite imagery, since it is impractical to acquire the flood area through field observations. This paper presents a method to flood extent extraction from synthetic-aperture radar (SAR) images that is based on intelligent computations. In particular, we apply artificial neural networks, self-organizing Kohonen’s maps (SOMs), for SAR image segmentation and classification. We tested our approach to process data from three different satellite sensors: ERS-2/SAR (during flooding on Tisza river, Ukraine and Hungary, 2001), ENVISAT/ASAR WSM (Wide Swath Mode) and RADARSAT-1 (during flooding on Huaihe river, China, 2007). Obtained results showed the efficiency of our approach

PRACTICAL ISSUES OF SENSOR WEB IMPLEMENTATION AND GRIDIFICATION

In this paper we provide an overview of emerging Sensor Web paradigm and show several practical issues of using Sensor Web technologies for real-world tasks. Issues under study include sensor description using SensorML and database performance for serving observations data. This paper also shows an approach for integrating standard Sensor Observation Service with Globus Toolkit Grid platform.\ud В данной работе представлен обзор развивающейся парадигмы Sensor Web и рассмотрены практические вопросы использования данной технологии для решения прикладных задач. Рассматриваются вопросы описания численных моделей с использованием языка SensorML и оценки производительности баз данных в задачах обслуживания сервисов Sensor Web. Кроме того, в работе описаны подходы к интеграции сервисов Sensor Web с Grid-платформой Globus Toolkit.\u

Data Assimilation Technique For Flood Monitoring and Prediction

This paper focuses on the development of methods and cascade of models for flood monitoring and forecasting and its implementation in Grid environment. The processing of satellite data for flood extent mapping is done using neural networks. For flood forecasting we use cascade of models: regional numerical weather prediction (NWP) model, hydrological model and hydraulic model. Implementation of developed methods and models in the Grid infrastructure and related projects are discussed

Grid Approach to Satellite Monitoring Systems Integration

This paper highlights the challenges of satellite monitoring systems integration, in particular based on Grid platform, and reviews possible solutions for these problems. We describe integration issues on different levels: data integration level and task management level (job submission in terms of Grid). We show example of described technologies for integration of monitoring systems of Ukraine (National Space Agency of Ukraine, NASU) and Russia (Space Research Institute RAS, IKI RAN). Another example refers to the development of InterGrid infrastructure that integrates several regional and national Grid systems: Ukrainian Academician Grid (with Satellite data processing Grid segment) and RSGS Grid (Chinese Academy of Sciences)

Neural and statistical techniques for remote sensing image classification

This paper examines different approaches to remote sensing images classification. Included in the study are statistical approach, in particular Gaussian maximum likelihood classifier, and two different neural networks paradigms: multilayer perceptron trained with EDBD algorithm, and ARTMAP neural network. These classification methods are compared on data acquired from Landsat-7 satellite. Experimental results showed that to achieve better performance of classifiers modular neural networks and committee machines should be applied

Intelligent Model of User Behavior in Distributed Systems

We present a complex neural network model of user behavior in distributed systems. The model reflects both dynamical and statistical features of user behavior and consists of three components: on-line and off-line models and change detection module. On-line model reflects dynamical features by predicting user actions on the basis of previous ones. Off-line model is based on the analysis of statistical parameters of user behavior. In both cases neural networks are used to reveal uncharacteristic activity of users. Change detection module is intended for trends analysis in user behavior. The efficiency of complex model is verified on real data of users of Space Research Institute of NASU-NSAU