Computational Science and Its Applications - ICCSA 2011

The analysis of movement in the geographic space requires the development of systems and data models supporting storage and querying functions useful for the analysis of how movement evolves and how it is constrained by events or obstacles that emerge in space. In the past, the design of information systems has been often based on application driven approaches and restricted to specific types of positioning technologies, leading to a proliferation of a wide range of data models, database models and functionalities. To overcome this proliferation, this paper proposes a spatio-temporal information system that is independent of the application domain and aiming to abstract the positioning technology used for gathering data. Here we describe the initial design of the basic infrastructure of such a system, integrating a data model and its fundamental functionalities for the collection, storage, retrieval and visualization of positioning data. The results achieved so far are promising to demonstrate how the system is able to store positioning data with different formats, and in applying its functionalities to the loaded data.

In this paper, we present a semi-automatic SOLAP approach specially dedicated to the analysis of spatial model simulation results. We illustrate it on demographic and economic data of rural municipalities resulting from a model developed in the context of the European project PRIMA.

Taking timely measures for management of the natural resources requires knowledge of the dynamic environment and land use practices in the rapidly changing post- industrial world. We used the SLUETH urban growth modeling and a multi-criteria evaluation (MCE) technique to predict and allocate land available to landfill as affected by the dynamics of the urban growth. The city is Gorgan, the capital of the Golestan Province of Iran. Landsat TM and ETM+ data were used to derive past changes that had occurred in the city extent. Then we employed slope, exclusion zones, urban areas, transportation network and hillshade layer of the study area in the SLEUTH modeling method to predict town sprawl up to the year 2050. We applied weighted linear combination technique of the MCE to define areas suitable for landfill. Linking the results from the two modeling methods yielded necessary information on the available land and the corresponding location for landfill given two different scenarios of town expansion up to the year 2050. These included two scenarios for city expansion and three scenarios for waste disposal. The study proved the applicability of the modeling methods and the feasibility of linking their results. Also, we showed the usefulness of the approach to decision makers in proactively taking measures in managing the likely environment change and possibly directing it towards more sustainable outcomes. This also provided a basis for dynamic land use allocation with regards to the past, present and likely future changes.

This study compares three interpolation methods to create continuous surfaces that describe temperature trends in Bangladesh between years 1948 and 2007. The reviewed techniques include Spline, Inverse Distance Weighting (IDW) and Kriging. A statistical assessment based on univariate statistics of the resulting continuous surfaces indicates that there is little difference in the predictive power of these techniques making hard the decision of selecting the best interpolation method. A Willmott statistical evaluation has been applied to minimize this uncertainty. Results show that IDW performs better for average and minimum temperature trends and Ordinary Kriging for maximum temperature trends. Results further indicate that temperature has an increasing trend all over Bangladesh noticably in the northern and coastal southern parts of the country. The temperature follows an overall increasing trend of 1.06

o

C per 100 years.

In this paper we introduce the new configuration of the EU-ClueScanner model (EUCS100) that is designed for evaluating the impact of policy alternatives on the European territory at the high spatial resolution of 100 meters. The high resolution in combination with the vast extent of the model called for considerable reprogramming to optimize processing speed. In addition, the calibration of the model was revised to account for the fact that different spatial processes may be prominent at this more detailed resolution. This new configuration of EU-ClueScanner also differs from its predecessors in that it has increased functionalities which allow the modeller more flexibility. It is now possible to work with irregular regions of interest, composed of any configuration of NUTS 2 regions. The structure of the land allocation model allows it to act as a bridge for different sector and indicator models and has the capacity to connect Global and European scale to the local level of environmental impacts. The EUCS100 model is at the core of a European Land Use Modelling Platform that aims to produce policy-relevant information related to land use/cover dynamics.

Most popular web map services, such as Google Maps, serve pre-generated image tiles from a server-side cache. However, storage needs are often prohibitive, forcing administrators to use partial caches containing a subset of the total tiles. When the cache runs out of space for allocating incoming requests, a cache replacement algorithm must determine which tiles should be replaced. Cache replacement algorithms are well founded and characterized for general Web documents but spatial caches comprises a set of specific characteristics that make them suitable to further research. This paper proposes a cache replacement policy based on neural networks to take intelligent replacement decisions. Neural networks are trained using supervised learning with real data-sets from public web map servers. Hight correct classification ratios have been achieved for both training data and a completely independent validation data set, which indicates good generalization of the neural network. A benchmark of the performance of this policy against several classical cache management policies is given for discussion.

The historical centre of Rome is characterized by the presence of high thickness of anthropic cover with scarce geotechnical characteristics. This anthropic backfill could induce damages in urban areas, i.e. mainly differential settlements and seismic amplifications. About 1400 measurements from boreholes stored in the UrbiSIT database have been used to re-construct the anthropic backfill bottom surface by geostatistical techniques. The Intrinsic Random Functions of order k (IRF-k) was employed and compared with other interpolation methods (i.e. ordinary kriging and kriging with external drift) to determine the best spatial predictor. Furthermore, IRF-k allows to estimate by using an external drift as secondary information. The advantage of this method is that the modeling of the optimal generalized covariance is performed by using an automatic procedure avoiding the time-consuming modeling of the variogram. Furthermore, IRF-k allows the modeling of non stationary variables.

Urbanization can be considered as a particular environmental gradient that produces modifications in the structures and functions of ecological systems. In landscape analysis and planning there is a clear need to develop specific and comparable indicators permitting the spatio-temporal quantification of this gradient and the study of its relationships with the composition and configuration of other land uses. This study, integrating urban gradient modelling and landscape pattern analysis, aims to investigate the spatiotemporal changes induced by urbanization and by other anthropogenic factors. Unlike previous studies, based on the transect approach, landscape metrics are calculated diachronically within five contiguous zones defined along the urban to rural gradient and characterized by decreasing intervals of settlement density. The results show that, within the study area, urban sprawl and agricultural land simplification remain the dominant forces responsible for the landscape modifications that have occurred during the period under investigation.

The MOLAND land use model was used in several studies to simulate possible scenarios of future settlement patterns in the Greater Dublin Region (GDR). This paper compares the results of three different research outputs with ten possible scenarios for GDR urban development. Brief descriptions of the scenarios and probability maps combining these scenarios are presented. The suggested approach of scenario analysis can be used by planners and decision makers to get an idea of the most likely development areas in the region if several scenarios are under consideration. In addition, probability maps help to find areas where the decisions could have the most influence on development patterns with minimal efforts.

Regional scientists’ methods to partition space in functional areas meet complex system analysts’ methods to detect communities in networks. A common concern is the detection of hierarchical sets of clusters representing underlying structures. In this paper modularity optimization in complex networks is compared to polarized functional area definition through dominant flows. Different approaches to the significance of dominant flows are also tested, namely threshold and Multiple Linkage Analysis approaches.

Both methods are applied recursively in order to obtain a hierarchical clustering of municipalities in the PACA region (France) based on commuting flows in 1999. The comparison focuses on the geographical meaning of the results of the analyses. Modularity optimization and dominant flow results agree in many points and highlight the inadequacy of official methods integrating administrative boundaries in functional area definition. When they differ, they offer complementary views on the urban structure of the PACA region.

Along this article we are going to explain some interesting strategies to reach the quality required for the phenomena considered inside the feature catalogue for the new BTA data model proposed by the Spanish Cartographic Norms Committee. We will focus on guaranteeing network connectivity, data accuracy and topological relationships between geometries of different phenomena. To reach our goal we will use ArcGIS software suite and its spatial analyst extension, building our own rules trying to automate the process and avoiding spending time to other interested users reusing our data model topology. The final goal of this work is to promote the migration to the new data model to earn interoperability between every cartographic producer by providing tools for potential users.

The assignment of admissible degrees of change to the building stock of an historic centre is one of the most important factors to be taken into account when developing an urban plan, because it establishes the possibilities of making changes to an existing urban fabric, or not; and since urban cores formed in ancient times contain the most significant concentrations of high-quality historic/architectural heritage, it is necessary to identify a way of specifically breaking down the changes to each building that are possible. In the case of Como, analysis of the morphological and socioeconomic characteristics of the fabric of the walled town and its extramural historic villages, and then the application of multivariate geostatistics, produced one such classification of this context in the form of six possible scenarios for change, of which the most significant was selected as the most effective for setting in train a profound upgrading of Como’s built fabric, which is currently in a state of advanced degradation.

Geography Markup Language

(GML) has been established as the standard language for the transport, storage and modelling of geographic information. In this paper we study how to adapt the XPath query language to GML documents. With this aim, we have defined a

XPath based query language

which handles the

“semantic structure”

of GML. Our approach focuses on querying urban maps whose representation is based on GML. We have developed a system called

UALGIS

, in order to implement the approach. Such system stores GML documents by means of the PostGIS RDBMS. In order to execute semantic-based XPath queries we have defined a translation of the queries into SQL. Such translation takes into account the GML schema. Finally, the system allows to visualize the result. With this aim, the result of a query is exported to the

Keyhole Markup Language (KML)

format.

This paper analyzes the link between international migration and educational attainment of the Mexican youth at the municipality level in 2000. This approach examines spatial heterogeneity in such relationship by testing two regionalization proposals, through spatial regime models. On one hand, we test well-known hypothesis that geographical differences obey to the historical-migratory trajectory of each region. On the other hand, we propose a model that accounts for the spatial differences based on the interface of migration and labor markets performance. Results suggest a large, negative effect of international migration on educational achievement, and strong spatial heterogeneity in that association. Results from groupwise heteroskedastic spatial regimes support the second hypothesis, since it captures better the spatial variability, as well as the behavior of the international migration variable across these regimes. These outcomes highlight the need to use proper geostatistical methods to examine territorial disparities.

Accessibility is an important factor in the development of land-use patterns and urban settlements, and these two components are considered linked close together. With the increasing attention payed to reduction of CO2 emissions, focus has been turned towards increased accessibility to public transportation. The work conducted in this study proposes a simplistic raster based model of accessibility to workplaces based on the public transportation services. The raster GIS based approach was chosen due to the need to combine this model with a raster based land-use simulation framework, where simulations of future urban development could be modelled. It has been shown that the proposed GIS model developed is suitable for representing the spatial difference in terms of accessibility for commuters by public transport within the study area. Clear differences between the Copenhagen Metropolitan area and the outskirts of the region can be seen, and are very much in line with statistics on commuting activities for the municipalities involved.

The paper describes the architecture and main concepts of a geo-referenced web 2.0 application with a strong social-network component and possibly integrated with handheld devices. The application, called MyTravel, allows the users to share their travel experiences, in terms of geographic information and publishing contents like photos, notes and comments. In the paper we present the domain model of the application, associated to the functional prerequisites; then, we describe the architecture, technologies and the integration techniques adopted in the project, that have been validated through the development of the application prototype.

A relevant issue in Remote Sensing (RS) and GIS is related to the analysis and the characterization of Land Use Land Cover (LULC) changes, very useful for a wide range of environmental applications and to efficiently undertake landscape planning and management policies. The methodology described has been applied to a case-study conducted in the area of the Province of Avellino (Southern Italy). Firstly, aerial photos and Landsat imagery have been classified to produce LULC maps for a fifty-year period (1954÷2004). Then, through a GIS approach, change detection and spatiotemporal analysis has been integrated to characterize LULC dynamics, focusing on the urban-rural gradient. This study has shown that LULC patterns and their changes are linked to both natural and social processes whose driving role has been clearly demonstrated: after the disastrous Irpinia earthquake (1980), local specific zoning laws and urban plans have significantly addressed landscape changes.

The main purpose of SOLAP concept was to take advantage of the map visualization improving the analysis of data and enhancing the associated decision making process. However, in this environment, the map can easily become cluttered losing the benefits that triggered the appearance of this concept. In order to overcome this problem we propose a post-processing stage, which relies on a spatial clustering approach, to reduce the number of values to be visualized when this number is inadequate to a properly map analysis. The results obtained so far show that the usage of the post–processing stage is very useful to maintain a map suitable to the user’s cognitive process. In addition, a novel heuristic to identify the threshold value from which the clusters must be generated was developed.

This research maps the urban quality of life (QoL) in Alfama, Lisbon (Portugal) through objective and subjective measures. A survey of 69 respondents and locations of social services were gathered suggesting the subjective and objective QoL respectively in the physical, economic, and social domains. The relationship between the two measures is examined using correlation analysis. It was determined that the association between them is weak and not significant, which could have been caused by the geographic scale and the sample size. These two factors also affected the spatial autocorrelation check implemented to the 15 subjective indicators using the Moran’s I test. Out of 15, only 3 indicators were spatially autocorrelated. These 3 indicators were interpolated using Ordinary Kriging (OK). The rest is interpolated using the Voronoi polygon. All 15 prediction maps were used to create the overall subjective QoL using Weighted Sum procedure.

To understand the evolution trends of landscape, in particular those linked to urban/rural relations, is crucial for a sustainable landscape planning. The main goal of this paper is to interpret the forest landscape dynamics occurred over the period 1955÷2006 in the municipality of Serra San Bruno (Calabria, Italy), an area of high environmental interest. The peculiarity of the analysis is the high level of detail of the research (minimum mapping unit 0.2 ha). Data were obtained through the digitisation of historical aerial photographs and digital orthophotos by homogenising Land Use/Land Cover (LULC) maps according to the Corine Land Cover legend. The investigated period was divided into four significant time intervals, which were specifically analysed to detect LULC changes. The use of thematic overlays and transition matrices enabled a precise identification of the LULC changes that had taken place over the examined period. As a result, detailed description and mapping of the landscape dynamics occurred over the same period were obtained.

Constructing and improving urban residential areas is an eternal critical subject in the process of the whole urban development which is connected with a series of challenges and problems. In this paper, firstly DPSIR (Driving Forces-Pressure-State-Impact-Response) framework has been employed for better systematizing the indicators on residential sustainability. Due to the urban activities cause impacts not only on local level but also a broader scale, a simulation model, using System Dynamics (SD) methodology, is structured to quantitatively investigate the developmental tendency of the indicators. And then the estimated results were shown in 2D density maps in ArcGIS and 3D visualization in CityEngine. The integration of GIS, SD model and 3D, called GISSD system here, can better explain the interaction and the variation in time of the sustainability indicators for residential development. Hence it’s able to support the Decision Maker to view the sustainable level of urban residential areas more comprehensively.

One of geopolitics realizations is the establishment of international borders between countries. Indonesia has international land borders with 3 countries, including the land border with Timor-Leste.

Establishment of the international border between Indonesia and Timor-Leste was a joint mandate of the two Governments, these are the Republic of Indonesia (RI) and the Democratic Republic of Timor-Leste (RDTL) based on the Treaty 1904 and the Arbitrary Awards 1914. Joint border surveys have been in progress achieving 96% of the total length of the border lines. During the surveys there were problematic situations occurred.

A government’s geopolitics is not always fortunate to immediately match with the local’s concern on the land border line establishment. This is particularly happening at a certain land border line segment between Indonesia and Timor-Leste in West Sector. This paper does not describe which side is right or wrong, instead, a soft approach of solution is underlined.

The last decades have seen a steady increase of digital spatial data and their effective availability. Embedded in the rapid developments in information and communication technology (ICT) such geospatial data or geodata are globally accessible, mainly via the internet, in a magnitude unseen before. In a parallel development of geographical information systems, computer-assisted cartography and the internet, a vast variety of web-based services have emerged to capture, store, analyse and present geodata. The map output from these systems is frequently suboptimal, lacking graphic expressiveness and effectiveness. This paper discusses a web-based service framework, the CartoService, to improve the geovisualisation quality of mapped geodata and provide laypersons and professionals with quality map graphics.

Over recent years, and related in particular to the significant recent international economic crisis, an increasingly worrying rise in poverty levels has been observed both in Italy, as well as in other countries. Such a phenomenon may be analysed from an objective perspective (i.e. in relation to the macro and micro-economic causes by which it is determined) or, rather, from a subjective perspective (i.e. taking into consideration the point of view of individuals or families who locate themselves as being in a condition of hardship). Indeed, the individual “perception” of a state of being allows for the identification of measures of poverty levels to a much greater degree than would the assessment of an external observer. For this reason, experts in the field have, in recent years, attempted to overcome the limitations of traditional approaches, focusing instead on a multidimensional approach towards social and economic hardship, equipping themselves with a wide range of indicators on living conditions, whilst simultaneously adopting mathematical tools which allow for a satisfactory investigation of the complexity of the phenomenon under examination. The present work elaborates on data revealed by the EU-SILC survey of 2006 regarding the perception of poverty by Italian families, through a fuzzy regression model, with the aim of identifying the most relevant factors over others in influencing such perceptions.

A fuzzy Inference System (FIS) was developed to generate recommendations for spatially variable applications of nitrogen (N) fertilizer using soil, plant and precipitation information. Experiments were conducted over three seasons (2005-2007) to assess the effects of soil electrical conductivity (ECa), nitrogen sufficiency index (NSI), and precipitations received in the vicinity of N fertilizers application, on response to N measured at mid-season growth. Another experiment was conducted in 2010 to understand the effect of water supply (WS) on response to N, using a spatially variable irrigation set-up. Better responses to N were observed in the case of high ECa, low NSI and high WS. In the opposite cases (low ECa, high NSI or low WS), nitrogen fertilizer rates can be reduced. Using fuzzy logic, expert knowledge was formalized as a set of rules involving ECa, NSI and cumulative precipitations to estimate economically optimal N rates (EONR).

Over the last decade the need for public bodies to characterise the vitality and degree of sustainability of their territories is well acknowledged. Still it remains unclear how to integrate the different categories of values of our daily life places in a comprehensive way in order to develop appropriate and well balanced policies. An experimental case has been designed to provide novel sets of indicators by integrating information extracted from custom maps, spatial descriptors of land use and land cover and socio-economic indicators. In order to fully grasp the character of a living place, the nuances of less tangible aspects should be also understood. To do so, the results developed during first steps have been subsequently refined by incorporating relevant volunteering geographical information available on Google Earth® platform.

Urban morphology plays a crucial role in the alteration of the local climate, resulting in the formation of Urban Heat Islands. Regarding the steady growth of cities and the impact of global climate change, the risk of overheating is expected to increase. In order to reduce this risk and the resulting health problems, planning measures are needed to adapt to these severe events. Planners however need tools to quantify and evaluate different adaption strategies to judge its effectiveness. This paper presents conceptual thoughts towards the development of such a planning tool, as well as proposing a method to derive potential areas of intra Urban Heat Islands. A first calibration of the landscape metric for city of Hamburg region is presented, which showcases the method.

This paper raises the issue of a research work oriented to the storage, retrieval, representation and analysis of dynamic GI, taking into account the semantic, the temporal and the spatiotemporal components. We intend to define a set of methods, rules and restrictions for the adequate integration of these components into the primary elements of the GI: theme, location, time [62]. We intend to establish and incorporate three new structures into the core of data storage by using mark-up languages: a semantic-temporal structure, a geosemantic structure, and an incremental spatiotemporal structure. The ultimate objective is the modelling and representation of the dynamic nature of geographic features, establishing mechanisms to store geometries enriched with a temporal structure (regardless of space) and a set of semantic descriptors detailing and clarifying the nature of the represented features and their temporality. Thus, data would be provided with the capability of pinpointing and expressing their own basic and temporal characteristics, enabling them to interact with each other according to their context, and their time and meaning relationships that could be eventually established. All of this with the purpose of enriching GI storing and improving the spatial and temporal analyses.

Nowadays, the most common approach to seismic risk mitigation is characterized only by strategies reducing building vulnerability, through structural interventions, and it does not consider the possibility to intervene at urban scale, reducing urban seismic vulnerability. This paper deals with the concept of urban seismic vulnerability, and introduces resilience, as the capacity of a system to adapt itself to new, generally negative, conditions, in order to re-establish normal conditions. Each city can express resilience, and the identification of its elements is the goal of our research. A spatial multi-criteria approach is here proposed.

The flows of carbon and energy produced by urbanized areas represent one of the aspects of urban sustainability that can have an important impact on climate change. For this reason, in recent years the quantitative estimation of the so-called urban metabolism components has increasingly attracted the attention of researchers from different fields. On the other hand, it has been well recognized that the structure and design of future urban development can significantly affect the flows of material and energy exchanged by an urban area with its surroundings. In this context, the paper discusses a software framework able to estimate the carbon exchanges accounting for alternative scenarios which can influence urban development. The modelling system is based on four main components: (i) a Cellular Automata model for the simulation of the urban land-use dynamics; (ii) a transportation model, able to estimate the variation of the transportation network load and (iii) the ACASA (Advanced Canopy-Atmosphere-Soil Algorithm) model which was tightly coupled with the (iv) mesoscale weather model WRF for the estimation of the relevant urban metabolism components. An in-progress application to the city of Florence is presented and discussed.

Nowadays, transport contributes significantly towards environmental problems (about 50% of the total CO and NOx). To date, most environmental issues due to transport have focused on general transportation methods. On the other hand, a popular approach of transport – Demand Responsive Transport (DRT) – has been studied for various aspects but rarely from the perspective of environmental issues. In this paper, we investigate the impacts of DRTs on pollutant emissions. For this purpose, we adapt a method established by European research co-operation - Methodologies for Estimation of Emissions from Transport (MEET). We create a specific model to estimate the pollution of a DRT system (GREEN-DRT) adapted from the MEET. We simulate DRT operation on three overlapping territories in France. The results show that optimising the DRT induces a significant decrease of pollutant emission due to the reduction of vehicles and travelled distances.

The rapid developments in the availability and access to spatially referenced information in a variety of areas, has induced the need for better analysis techniques to understand the various phenomena. In particular our analysis represents a first insight into a wealth of geographical data collected by individuals as activity dairy data.The attention is drawn on point datasets corresponding to GPS traces driven along a same route in different days. Our aim here is to explore the presence of clusters along the route, trying to understand the origins and motivations behind that in order to better understand the road network structure in terms of ’dense’ spaces along the network. In this paper the attention is therefore focused on methods to highlight such clusters and see their impact on the network structure. Spatial clustering algorithms are examined (DBSCAN) and a comparison with other non-parametric density based algorithm (Kernel Density Estimation) is performed. A test is performed over the urban area of Trieste (Italy).

The paper explains a procedure to discover the coherence of the relationship between physical distance and real estate value variation.

Many author consider (both in the past and in the recent time) the possibility that real estate value can depend on distance from some central point.

Such convintion lead to the use of geostatistical approaches based on kriging techniques. In the same time literature teach that the market shows a higher value where several amenities are coexisting.

But in those urban realities where the number of central points and the number of amenities is high, the complexity does not support the construction of models, and this complexity leads to a different concept of identity as synthesis of distance, borders and concentration.

The use of fuzzy cluster can support the analysis. The paper gives a brief example about how this works in the case of New York core.

In the great majority of urban models based on Cellular Automata (CA), the concept of proximity is assumed to reflect two fundamental sources of spatial interaction: (1) accessibility and (2) vicinity in Euclidean sense. While the geographical space defined by the latter clearly has an Euclidean representation, the former, based on the accessibility, does not admit such a regular representation. Very little operational efforts have been undertaken in CA-based urban modelling to investigate and provide a more coherent and cogent treatment of such irregular geometries, which indeed are a fundamental feature of any urban geography. In this paper, we suggest an operational approach – entirely based on cellular automata techniques – to model the complex topology of proximities arising from urban geography, and to entangle such proximity topology with a CA model of spatial interactions.

After the introduction of digital mapping techniques in the 1960s and then GIS shortly afterwards, researchers realized that error and uncertainty in digital spatial data had the potential to cause problems that had not been experienced with paper maps [1]. Spatial data quality is a very active domain in geographic information research. This paper describes the significance of data quality and how is data quality defined. Data conflation can help to increase the amount of suitable for usage data. This paper analyzes results of spatial data conflation.

People establish connections with the territory that live in and the territory is modelled by their presence. The sedimentation of the places, by chance or planned, get

inhabited

people according to models and connections that are deeply linked to the individual and social experience. The digital territories as

social-network

seem to set up similar connections as the traditional social dynamics do with the physical territory.

But how are they able to (re)produce significant experiences for the different

social-tribes

that in the territory interact and communicate?

These questions are investigated by this study carried out on the field and focused on Milano-Bicocca district.

The study consists of two parts: a qualitative/ hybrid research about the

urban-island

to understand the connections that the social groups establish with the space and between themselves. The second –

design-oriented

– proposes some models of representation and tools for planning web communication shaped on the

social-tribes

and their relational experience.

The growing amount of various kinds of information triggers the need to develop efficient network computing systems, as single machines in many cases are not able to provide effective processing and analysis. One of the very promising approaches of distributed data analysis is combined classification, which could be relatively easily implemented in distributed computing systems. In this paper we address problem of decentralized distributed computing system for mentioned above classification method. We focus on the system fairness. The performance metric is defined as a maximum response time, i.e., the computing system should be designed to minimize the response time of each client using the system. We assume that the system is decentralized and each request is sent by the client directly to computing nodes without assistance of a central service. An ILP (Integer Linear Programming) model is formulated and applied to obtain optimal results provided by branch-and-cut algorithm included in the CPLEX solver. Widespread simulations are performed to evaluate properties of the computing system in terms of several parameters describing the system.

This paper proposes an innovative combinational algorithm to improve the performance of multiclass problems. Because the more accurate classifier the better performance of classification, so researchers have been tended to improve the accuracies of classifiers. Although obtaining the more accurate classifier is often targeted, there is an alternative way to reach for it. Indeed one can use many inaccurate classifiers each of which is specialized for a few dataitems in the problem space and then s/he can consider their consensus vote as the classification. This paper proposes a new ensembles methodology that uses ensemble of classifiers as elements of ensemble. These ensembles of classifiers jointly work using majority weighted voting. The results of these ensembles are in weighted manner combined to decide the final vote of the classification. In empirical result, these weights in final classifier are determined with using a series of genetic algorithms. We evaluate the proposed framework on a very large scale Persian digit handwritten dataset and the results show effectiveness of the algorithm.

Today, as the scale of network grows up, a standalone NIDS with only one intrusion detection node is not enough to inspect all traffic. One of the most widely considered solutions to address this problem is to configure parallel NIDS in which multiple intrusion detection nodes work together. A load balancing mechanism enables this configuration by distributing traffic load to several nodes. In the frequently changing environment of today’s network, it is an important issue for load balancing mechanism to distributing traffic equally to each node. Meanwhile, several studies have been made on the load balancing scheme, but they do not satisfy the requirements of load balancing for parallel NIDS. Thus we proposed HLPN (Hash-based Load balancing scheme suitable for Parallel NIDS) which satisfies these requirements. As a result of the performance evaluation, HLPN represented 58% better performance in terms of the fairness of the traffic distribution than static hash-based scheme, and gave almost equal, or rather better, performance to that of DHFV.

Very often symbolic regression, as addressed in Genetic Programming (GP), is equivalent to approximate interpolation. This means that, in general, GP algorithms try to fit the sample as better as possible but no notion of generalization error is considered. As a consequence, overfitting, code-bloat and noisy data are problems which are not satisfactorily solved under this approach. Motivated by this situation we review the problem of Symbolic Regression under the perspective of Machine Learning, a well founded mathematical toolbox for predictive learning. We perform empirical comparisons between classical statistical methods (AIC and BIC) and methods based on Vapnik-Chrevonenkis (VC) theory for regression problems under genetic training. Empirical comparisons of the different methods suggest practical advantages of VC-based model selection. We conclude that VC theory provides methodological framework for complexity control in Genetic Programming even when its technical results seems not be directly applicable. As main practical advantage, precise penalty functions founded on the notion of generalization error are proposed for evolving GP-trees.

In this paper, a procedure of decomposition of nonlinearly filtered sequences in primary characteristic sequences has been introduced. Such a procedure allows one to analyze structural properties of the filtered sequences e.g. period and linear complexity, which are essential features for their possible application in cryptography. As a consequence of the previous decomposition, a simple constructive method that enlarges the number of known filtered sequences with guaranteed cryptographic parameters has been developed. The procedure here introduced does not impose any constraint on the characteristics of the nonlinear filter.

In this paper we propose high throughput low collision, mobility adaptive and energy efficient medium access protocol (MAC) called Mobility Adaptive (MA-CSMA/CA) for wireless sensor networks. MA-CSMA/CA ensures that transmissions incur less collision, and allows nodes to undergo sleep mode whenever they are not transmitting or receiving. It uses contention based as well as contention free period efficiently together to minimise the number of collisions cause by the mobile node entering and leaving the clusters. It also allows nodes to determine when they can switch to sleep mode during operation. MA-CSMA/CA for mobile nodes provides fast association between the mobile node and the cluster coordinator. The performance of MA-CSMA/CA is evaluated through extensive simulation, analysis and compared with the existing IEEE 802.15.4 industrial standard. The results show that MA-CSMA/CA outperforms significantly the existing CSMA/CA protocol including throughput, latency and energy consumption.

This paper illustrates using Rough set theory as a data mining method for modeling Alert systems. A data-driven approach is applied to design a reliable alert system for prediction of different situations and setting off of the alerts for various critical parts of human industry sections. In this system preprocessing and reduction of data with data mining methods is performed. Rough set learning method is used to attain the regular and reduced knowledge from the system behaviors. Finally, using the produced and reduced rules extracted from rough set reduction algorithms, the obtained knowledge is applied to reach this purpose. This method, as demonstrated with successful realistic applications, makes the present approach effective in handling real world problems. Our experiments indicate that the proposed model can handle different groups of uncertainties and impreciseness accuracy and get a suitable predictive performance when we have several certain features set for representing the knowledge.

A model, discrete in terms of time, geometrical space and velocity, is defined for a mix of car and bicycle traffic. Although based on cellular automata (CA) and interchangeable with a CA model in some special cases, the spatial aspect of the model presented here includes some characteristics that set it apart from CA models, such as overlapping cells and extended stochasticity. These characteristics allow easy incorporation of a variety of network elements into a spatial network model. The behaviour model includes rules for movement along stretches of road, as well as rules of behaviour at decision and conflict points on the road. Agent based simulations are run for three simple scenarios and results of these simulations are presented.

Similarity search in a large collection of stored objects in a metric database has become a most interesting problem. The

Spaghettis

is an efficient metric data structure to index metric spaces. However, for real applications processing large volumes of generated data, query response times can be high enough. In these cases, it is necessary to apply mechanisms in order to significantly reduce the average query time. In this sense, the parallelization of metric structures is an interesting field of research. The recent appearance of

GPU

s for general purpose computing platforms offers powerful parallel processing capabilities. In this paper we propose a

GPU

-based implementation for

Spaghettis

metric structure. Firstly, we have adapted

Spaghettis

structure to

GPU

-based platform. Afterwards, we have compared both sequential and

GPU

-based implementation to analyse the performance, showing significant improvements in terms of time reduction, obtaining values of speed-up close to 10.

This work presents an object-oriented approach to the concurrent computation of eigenvalues and eigenvectors in real symmetric and Hermitian matrices on present memory shared multicore systems. This can be considered the lower level step in a general framework for dealing with large size eigenproblems, where the matrices are factorized to a small enough size. The results show that the proposed parallelization achieves a good speedup in actual systems with up to four cores. Also, it is observed that the limiting performance factor is the number of threads rather than the size of the matrix. We also find that a reasonable upper limit for a “small” dense matrix to be treated in actual processors is in the interval 10000-30000.

With the development of web based technology and availability of spatial data infrastructure, the demand for accessing geospatial information over web has increased significantly. The data sets are being accessed by standard geospatial web services. However, for complex user queries involving multiple web services, it is required to discover those services and logically compose them to deliver the intended information. In this paper, a geospatial orchestration engine has been proposed for composition geospatial web services. A rule repository has been designed within the orchestration engine by considering the basic geospatial web services. A framework has been presented to develop a complex information service which can be achieved by chaining of already existing services. The efficacy of the framework has been demonstrated through a case study.

In recent years, information and communication technology (ICT) has been characterised by several evolving trends and new challenges. The process towards the convergence has been developed to take into account new realities and new perspectives. Along with many positive benefits, there are several security concerns and ensuring privacy is extremely difficult. New security issues make it necessary to rewrite the safety requirements and to know what the risks are and what can be lost. With this paper we want to propose a bio-inspired approach as a result of a comparison between biological models and information security. The risk analysis proposed aims to address technical, human and economical aspects of the security to strategically guide security investments. This analysis requires knowledge of the failure time distribution to assess the degree of system security and analyse the existing countermeasures to decrease the risk, minimise the losses, and successfully manage the security.

The issues of survivability of networks, especially to some open year round services have increased rapidly over the last few years. To address this topic, the effective survivability metric is mandatory for managerial responsibility. In this paper, we provide a survivability mechanism called Degree of Disconnectivity (DOD) for the network operator to detect risks. To evaluate and analyze the robustness of a network for network operators, this problem is modeled as a mathematical programming problem. An attacker applies his limited attack power intelligently to the targeted network. The objective of the attacker is to compromise nodes, which means to disable the connections of O-D pairs, to achieve the goal of reaching a given level of the proposed Degree of Disconnectivity metric. A Lagrangean Relaxation-based algorithm is adopted to solve the proposed problem.

This paper shows the application of generalized finite difference method (GFDM) to the problem of dynamic analysis of plates. We investigated stability and its relation with the irregularity of a cloud of nodes.

Tensor field visualization is a hard task due to the multivariate data contained in each local tensor. In this paper, we propose a particle-tracing strategy to let the observer understand the field singularities. Our method is a viewer-dependent approach that induces the human perceptual system to notice underlying structures of the tensor field. Particles move throughout the field in function of anisotropic features of local tensors. We propose a easy to compute, viewer-dependent, priority list representing the best locations in tensor field for creating new particles. Our results show that our method is suitable for positive semi-definite tensor fields representing distinct objects.

In this paper a modified discrete-time chaotic system is presented from the statistical point of view. This chaotic system is used in a cryptosystem and, for improving the presented method used in security data transmission, its structure is changed. The technique is implemented for a Rössler hyperchaotic system. The improvement consists in modifying the existing system in order to obtain a higher robustness for the cryptosystem; for this, a time varying delay is added in its structure.