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Congress on Evolutionary Computation

作者： Edgar Galván-López Brendan Cody-Kenny Leonardo Trujillo Ahmed Kattan Distributed Systems Group School of Computer Science and Statistics Trinity College Dublin Ireland Doctorado en Ciencias de la Ingeniería Instituto Tecnológico de Tijuana Mexico AI Real-World Applications Lab Computer Science Department UQU Saudi Arabia

ISBN: (纸本)9781479904532

Research on semantics in Genetic Programming (GP) has increased over the last number of years. Results in this area clearly indicate that its use in GP considerably increases performance. Many of these semantic-based approaches rely on a trial-and-error method that attempts to find offspring that are semantically different from their parents over a number of trials using the crossover operator (crossover-semantics based - CSB). This, in consequence, has a major drawback: these methods could evaluate thousands of nodes, resulting in paying a high computational cost, while attempting to improve performance by promoting semantic diversity. In this work, we propose a simple and computationally inexpensive method, named semantics in selection, that eliminates the computational cost observed in CSB approaches. We tested this approach in 14 GP problems, including continuous- and discrete-valued fitness functions, and compared it against a traditional GP and a CSB approach. Our results are equivalent, and in some cases, superior than those found by the CSB approach, without the necessity of using a “brute force” mechanism.

关键词： Semantics Sociology Statistics Vectors Genetic programming Computational efficiency Context

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POSTER: Towards measuring warning readability

POSTER: Towards measuring warning readability

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2012 ACM Conference on computer and Communications Security, CCS 2012

作者： Harbach, Marian Fahl, Sascha Muders, Thomas Smith, Matthew Distributed Systems and Security Group Department of Computer Science Leibniz Universität Hannover Germany

ISBN: (纸本)9781450316507

Security systems frequently rely on warning messages to convey important information, especially when a machine is not able to assess a situation automatically. For a long time, researchers have investigated the effects of warning messages to optimise their reception by a user. Design guidelines and best practises help the developer or interaction designer to adequately channel urgent information. In this poster, we investigate the application of readability measures to assess the difficulty of the descriptive text in warning messages. Adapting such a measure to fit the needs of warning message design allows objective feedback on the quality of a warning's descriptive text. An automated process will be able to assist software developers and designers in creating more readable and hence more understandable security warning messages. We present an initial exploration of the use of readability measures on the descriptive text of warning messages. Existing measures were evaluated on warning messages extracted from current browsers using an experimental study with 15 undergrad students. While our data did not yield conclusive results yet, we argue that readability measures can provide valuable assistance when implementing security systems. Copyright © 2012 ACM.

关键词： Security systems

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Making cost-based query optimization asymmetry-aware

Making cost-based query optimization asymmetry-aware

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8th International Workshop on Data Management on New Hardware, DaMoN 2012 - In Conjunction with ACM SIGMOD/PODS Conference

作者： Bausch, Daniel Petrov, Ilia Buchmann, Alejandro Technische Universität Darmstadt Department of Computer Science Databases and Distributed Systems Group Hochschulstraße 10 64289 Darmstadt Germany

ISBN: (纸本)9781450314459

The architecture and algorithms of database systems have been built around the properties of existing hardware technologies. Many such elementary design assumptions are 20 - 30 years old. Over the last five years we witness multiple new I/O technologies (e.g. Flash SSDs, NV-Memories) that have the potential of changing these assumptions. Some of the key technological differences to traditional spinning disk storage are: (i) asymmetric read/write performance;(ii) low latencies;(iii) fast random reads;(iv) endurance issues. Cost functions used by traditional database query optimizers are directly influenced by these properties. Most cost functions estimate the cost of algorithms based on metrics such as sequential and random I/O costs besides CPU and memory consumption. These do not account for asymmetry or high random read and inferior random write performance, which represents a significant mismatch. In the present paper we show a new asymmetry-aware cost model for Flash SSDs with adapted cost functions for algorithms such as external sort, hash-join, sequential scan, index scan, etc. It has been implemented in PostgreSQL and tested with TPC-H. Additionally we describe a tool that automatically finds good settings for the base coefficients of cost models. After tuning the configuration of both the original and the asymmetry-aware cost model with that tool, the optimizer with the asymmetry-aware cost model selects faster execution plans for 14 out of the 22 TPC-H queries (the rest being the same or negligibly worse). We achieve an overall performance improvement of 48% on SSD. © 2012 ACM.

关键词： Cost functions

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Interconnectedness of complex systems of internet of things through social network analysis for disaster management

Interconnectedness of complex systems of internet of things ...

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2012 4th International Conference on Intelligent Networking and Collaborative systems, INCoS 2012

作者： Zelenkauskaite, Asta Bessis, Nik Sotiriadis, Stelios Asimakopoulou, Eleana Department of Telecommunications Indiana University Bloomington IN United States Distributed and Intelligent Systems Research Group University of Derby Derby United Kingdom Department of Computer Science and Technology University of Bedfordshire Luton United Kingdom

ISBN: (纸本)9780769548081

This visionary paper presents the Internet of Things paradigm in terms of interdependent dynamic dimensions of objects and their properties. Given that in its current state Internet of Things (IoT) has been viewed as a paradigm based on hierarchical distribution of objects, evaluation of the dynamic nature of the hierarchical structures faces challenges in its evaluation and analysis. Within this in mind, our focus is on the area of complex social networks and the dynamic social network construction within the context of IoT. This is by highlighting and addressing the tagging issues of the objects to the real-world domain such as in disaster management, these are in relation to their hierarchies and interrelation within the context of social network analysis. Specifically, we suggest to investigate and deepen the understanding of the IoT paradigm through the application of social network analysis as a method for interlinking objects - and thus, propose ways in which IoT could be subsequently interlinked and analyzed through social network analysis approach - which provides possibilities for linking of the objects, while extends it into real-world domain. With this in mind, we present few applications and key characteristics of disaster management and the social networking analysis approach, as well as, foreseen benefits of its application in the IoT domain. © 2012 IEEE.

关键词： Internet of things

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Disaster management and profile modelling of IoT objects: Conceptual parameters for interlinked objects in relation to social network analysis

Disaster management and profile modelling of IoT objects: Co...

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2012 4th International Conference on Intelligent Networking and Collaborative systems, INCoS 2012

ISBN: (纸本)9780769548081

In recent years, the emergence of ubiquitous and pervasive computing suggests the radical transformation of the Internet to incorporate physical objects. The transformation suggests the enabling of a new form of communication and interaction style that incorporates people, their smart devices and their physical objects through the utilization of distributed sensors spread in the environment. In this study, we propose extending Internet of Things (IoT) by modelling an IoT enabled smart environment as a whole, representing the dynamic communication and interaction among all objects (users, devices, physical objects and sensors). This is by recognizing and categorizing objects' properties in the form of a generic profile. We also reflect this in a disaster management context. That is by identifying which of the parameters of these smart objects are fixed, constant and persistent over time and which parameters are actually change over time, i.e. those characterized by their transient and dynamic nature. Thus, through the process of communication and interaction of the objects, we analyze parameters by demonstrating their static and/or dynamic properties as well as those supporting context-aware variables which are evident in disaster scenarios. To achieve these goals, we designed the persistent or temporal relationships to encompass internal information of smart-objects, along with their characteristics that actually depict their capacity to offer services to users by properties' matchmaking. The interlinked relationships represent a 'social network' providing a terrain of flexible scenarios that would lead to tailored parameters to fit user preferences. To enable communication among them in a dynamic dimension we utilized a distributed topology in which communication could occur indirectly between objects. Finally, we detailed a generic - but equally applicable for disaster management - case scenario in which we used graph theory to demonstrate how embedded intelligenc

关键词： Internet of things

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System monitoring with metric-correlation models

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IEEE Transactions on Network and Service Management 2011年第4期8卷 348-360页

作者： Jiang, Miao Munawar, Mohammad A. Reidemeister, Thomas Ward, Paul A.S. Shoshin Distributed Systems Group Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada

Modern software systems expose management metrics to help track their health. Recently, it was demonstrated that correlations among these metrics allow errors to be detected and their causes localized. Prior research shows that linear models can capture many of these correlations. However, our research shows that several factors may prevent linear models from accurately describing correlations, even if the underlying relationship is linear. Common phenomena we have observed include relationships that evolve, relationships with missing variables, and heterogeneous residual variance of the correlated metrics. Usually these phenomena can be discovered by testing for heteroscedasticity of the underlying linear models. Such behaviour violates the assumptions of simple linear regression, which thus fail to describe system dynamics correctly. In this paper we address the above challenges by employing efficient variants of Ordinary Least Squares regression models. In addition, we automate the process of error detection by introducing the Wilcoxon Rank-Sum test after proper correlations modeling. We validate our models using a realistic Java-Enterprise-Edition application. Using fault-injection experiments we show that our improved models capture system behavior accurately. © 2011 IEEE.

关键词： Fault detection

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Evolving radial basis function networks via GP for estimating fitness values using surrogate models

Evolving radial basis function networks via GP for estimatin...

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Congress on Evolutionary Computation

作者： Ahmed Kattan Edgar Galvan Evolutionary Design and Optimisation Group Computer Science Department Um Al-Qura University Saudi Arabia Distributed Systems Group School of Computer Science and Statistics Trinity College Dublin Ireland

In real-world problems with candidate solutions that are very expensive to evaluate, Surrogate Models (SMs) mimic the behaviour of the simulation model as closely as possible while being computationally cheaper to evaluate. Due to their nature, SMs can be seen as heuristics that can help to estimate the fitness of a candidate solution without having to evaluate it. In this paper, we propose a new SM based on Genetic Programming (GP) and Radial Basis Function Networks (RBFN), called GP-RBFN Surrogate. More specifically, we use GP to evolve both: the structure of a RBF and its parameters. The SM evolved by our algorithm is tested in one of the most studied NP-complete problem (MAX-SAT) and its performance is compared against RBFN Surrogate, GAs, Random Search and (1+1) ES. The results obtained by performing extensive empirical experiments indicate that our proposed approach outperforms the other four methods in terms of finding better solutions without the need of evaluating a large portion of candidate solutions.

关键词： Training Mathematical model Computational modeling Standards Approximation methods Search problems Optimization

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Interconnectedness of Complex systems of Internet of Things through Social Network Analysis for Disaster Management

Interconnectedness of Complex Systems of Internet of Things ...

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International Conference on Intelligent Networking and Collaborative systems (INCOS)

作者： Asta Zelenkauskaite Nik Bessis Stelios Sotiriadis Eleana Asimakopoulou Department of Telecommunications Indiana University Bloomington USA Distributed and Intelligent Systems Research Group University of Derby Derby UK Department of Computer Science and Technology University of Bedfordshire Luton UK

This visionary paper presents the Internet of Things paradigm in terms of interdependent dynamic dimensions of objects and their properties. Given that in its current state Internet of Things (IoT) has been viewed as a paradigm based on hierarchical distribution of objects, evaluation of the dynamic nature of the hierarchical structures faces challenges in its evaluation and analysis. Within this in mind, our focus is on the area of complex social networks and the dynamic social network construction within the context of IoT. This is by highlighting and addressing the tagging issues of the objects to the real-world domain such as in disaster management, these are in relation to their hierarchies and interrelation within the context of social network analysis. Specifically, we suggest to investigate and deepen the understanding of the IoT paradigm through the application of social network analysis as a method for interlinking objects -- and thus, propose ways in which IoT could be subsequently interlinked and analyzed through social network analysis approach - which provides possibilities for linking of the objects, while extends it into real-world domain. With this in mind, we present few applications and key characteristics of disaster management and the social networking analysis approach, as well as, foreseen benefits of its application in the IoT domain.

关键词： Social network services Context Sensors Internet Disaster management Wireless sensor networks

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Disaster Management and Profile Modelling of IoT Objects: Conceptual Parameters for Interlinked Objects in Relation to Social Network Analysis

Disaster Management and Profile Modelling of IoT Objects: Co...

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International Conference on Intelligent Networking and Collaborative systems (INCOS)

关键词： Social network services Context Organizations Disaster management Sensors Object recognition Internet

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Handling missing values in data mining - A case study of heart failure dataset

Handling missing values in data mining - A case study of hea...

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International Conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： N. Poolsawad L. Moore C. Kambhampati J. G. F. Cleland Distributed Reliable Intelligent Systems Research Group (DRIS) Department of Computer Science University of Hull Hull UK Hull York Medical School Department of Cardiology University of Hull Hull UK

In this paper, we investigate the characteristics of a clinical dataset using feature selection and classification techniques to deal with missing values and develop a method to quantify numerous complexities. The research aims to find features that have high effect on mortality time frame, and to design methodologies which will cope with the following challenges: missing values, high dimensionality, and the prediction problem. The experimental results will be extended to develop prediction model for HF This paper also provides a comprehensive evaluation of a set of diverse machine learning schemes for clinical datasets.

关键词： Data mining Classification algorithms Predictive models Heart Artificial neural networks Accuracy Prediction algorithms

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