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20 Papers

Extending Hypervisor Architecture to Allow One Way Data Transfers from VMs to Hypervisors

Topics: Cloud Computing Architecture; Virtualization Technologies; Cloud Optimization and Automation

Authors: Mustafa Aydin and Jeremy Jacob

Affiliation: University of York, United Kingdom

Digital Object Identifier: 10.5220/0004958006040609

Proceedings of CLOSER 2014, ISBN: 978-989-758-019-2

Quick Abstract

We propose an alternative architecture to existing hypervisors, which allows for more data to be moved whilst requiring less work for hardware and networks. Our suggestion is to develop an extension to hypervisors for an interface which can allow data transfer one way from virtual machines to hypervisors. We argue that the ability to transfer data directly in this way can provide a number of beneﬁts to cloud users and providers, namely in the areas of security (conﬁdentiality, integrity, and through decreased overhead), reduced energy consumption, and better use of hardware resources.

Related Ontology Subjects/Areas/Topics (6)

Cloud Computing		Cloud Computing Enabling Technology		Fundamentals
Cloud Computing Architecture		Cloud Optimization and Automation		Virtualization Technologies

A Binary Neural Network Framework for Attribute Selection and Prediction

Topics: Pattern Recognition; Neural Network Software and Applications; Neural Based Data Mining and Complex Information Processing

Authors: Victoria J. Hodge , Tom Jackson and Jim Austin

Affiliation: University of York, United Kingdom

Digital Object Identifier: 10.5220/0004150705100515

Proceedings of NCTA 2012/IJCCI, ISBN: 978-989-8565-33-4

Quick Abstract

In this paper, we introduce an implementation of the attribute selection algorithm, Correlation-based Feature Selection (CFS) integrated with our k-nearest neighbour (k-NN) framework. Binary neural networks underpin our k-NN and allow us to create a unified framework for attribute selection, prediction and classification. We apply the framework to a real world application of predicting bus journey times from traffic sensor data and show how attribute selection can both speed our k-NN and increase the prediction accuracy by removing noise and redundant attributes from the data.

Related Ontology Subjects/Areas/Topics (21)

Artificial Intelligence	Health Engineering and Technology Applications	Neurotechnology, Electronics and Informatics
Artificial Intelligence and Decision Support Systems	Human-Computer Interaction	Pattern Recognition
Biomedical Engineering	Methodologies and Methods	Physiological Computing Systems
Biomedical Signal Processing	Neural Based Data Mining and Complex Information Processing	Sensor Networks
Computational Intelligence	Neural Network Software and Applications	Signal Processing
Data Manipulation	Neural Networks	Soft Computing
Enterprise Information Systems	Neurocomputing	Theory and Methods

DEVELOPMENTAL PLASTICITY IN CARTESIAN GENETIC PROGRAMMING BASED NEURAL NETWORKS

Authors: Maryam Mahsal Khan ¹ , Gul Muhammad Khan ¹ and Julian F. Miller ²

Affiliations: ¹ University of Engineering and Technology, Pakistan , ² University of York, United Kingdom

Digital Object Identifier: 10.5220/0003615204490458

Proceedings of ANNIIP 2011/ICINCO, ISBN: 978-989-8425-74-4

Quick Abstract

This work presents a method for exploiting developmental plasticity in Artificial Neural Networks using Cartesian Genetic Programming. This is inspired by developmental plasticity that exists in the biological brain allowing it to adapt to a changing environment. The network architecture used is that of a static Cartesian Genetic Programming ANN, which has recently been introduced. The network is plastic in terms of its dynamic architecture, connectivity, weights and functionality that can change in response to the environmental signals. The dynamic capabilities of the algorithm are tested on a standard benchmark linear/non-linear control problems (i.e. pole-balancing).

Intelligent Decision Support using Pattern Matching

Authors: Victoria J. Hodge , Tom Jackson and Jim Austin

Affiliation: University of York, United Kingdom

Digital Object Identifier: 10.5220/0004473000440054

Proceedings of FIATS-M 2011, ISBN: 978-989-8425-87-4

Quick Abstract

The aim of our work is to develop Intelligent Decision Support (IDS) tools and techniques to convert traffic data into intelligence to assist network managers, operators and to aid the travelling public. The IDS system detects traffic problems, identifies the likely cause and recommends suitable interventions which are most likely to mitigate congestion of that traffic problem. In this paper, we propose to extend the existing tools to include dynamic hierarchical and distributed processing; algorithm optimisation using natural computation techniques; and, using a meta-learner to short-circuit the optimisation by learning the best settings for specific data set characteristics and using these settings to initialise the GA.

MOBILE DEVICE AND INTELLIGENT DISPLAY INTERACTION VIA SCALE-INVARIANT IMAGE FEATURE MATCHING

Topics: Ubiquitous Computing Systems and Services; Human and Computer Interaction; Mobile Computing

Authors: Leigh Herbert ¹ , Nick Pears ¹ , Dan Jackson ² and Patrick Olivier ²

Affiliations: ¹ University of York, United Kingdom , ² Newcastle University, United Kingdom

Digital Object Identifier: 10.5220/0003368302070214

Proceedings of PECCS 2011, ISBN: 978-989-8425-48-5

Quick Abstract

We present further developments of our system that allows direction interaction between a camera-equipped hand-held device and a remote display. The essence of this system is the ability to estimate a planar projectivity between the remote display and the displayed image of that display on the handheld device. We describe how to achieve this by matching scale invariant SURF features across the two displays (remote and hand-held). We implement a prototype system and a drawing application and conduct both performance and usability evaluations. The feedback given indicates that our system is responsive, accurate and easy to use.

Related Ontology Subjects/Areas/Topics (9)

Applications and Services	Human and Computer Interaction	Mobile Computing
Computer Vision, Visualization and Computer Graphics	Human-Computer Interaction	Telecommunications
Enterprise Information Systems	Mobile and Pervasive Computing	Ubiquitous Computing Systems and Services

MODEL-BASED FAILURE ANALYSIS OF BUSINESS PROCESS

Topics: Software Engineering; Business Process Management; Engineering Information System

Authors: Xiaocheng Ge , Richard F. Paige and John A. McDermid

Affiliation: University of York, United Kingdom

Digital Object Identifier: 10.5220/0003496203880391

Proceedings of ICEIS 2011, ISBN: 978-989-8425-55-3

Quick Abstract

The success of most organisations are based on how well the organisation can engineer and execute their business processes in order to better manage the extra value that these processes can provide. However, business processes may fail to deliver the functions which are designed. In order to help the understanding of how and why a business process may fail, we considered the techniques in system safety engineering and integrated them with existing techniques of business process modelling and analysis. We proposed a framework of business process failure analysis. The kernel of the framework is that we extended the workflow net of a business process by modelling failures as coloured tokens and so that the failure behaviours of the business process can be simulated. In addition, we developed a tool based on the codes of an open source project to support the analysis. The applications of proposed technique have demonstrated that it is a powerful and easy-to-use technique for the management of business processes in large complex enterprise systems.

Related Ontology Subjects/Areas/Topics (11)

Artificial Intelligence	Enterprise Engineering	Knowledge-Based Systems
Business Process Management	Enterprise Information Systems	Software Engineering
e-Business	Information Systems Analysis and Specification	Symbolic Systems
Engineering Information System	Knowledge Management and Information Sharing

KAOS-B: A Goal-oriented Process Model for EIS

Authors: Malihe Tabatabaie , Fiona A. C. Polack and Richard F. Paige

Affiliation: University of York, United Kingdom

Digital Object Identifier: 10.5220/0003016000400049

Proceedings of MSVVEIS 2010/ICEIS, ISBN: 978-989-8425-12-6

Quick Abstract

There is increasing demand for approaches to develop more effective enterprise information systems (EIS). A possible solution is to focus on eliciting and elaborating goals prior to capturing EIS requirements. Focusing on EIS goals could help the developer team and other stakeholders (particularly decision makers) achieve a shared and structured understanding of the goals of the EIS and the overall enterprise. We present an investigation of the use of the KAOS approach to goal modelling in the domain of EIS. As a result, we propose a tailoring of KAOS for this domain; the tailoring has been developed through empirical studies. Our tailoring, called KAOS-B, is described, and an empirical evaluation is presented, investigating its applicability for defining and structuring the goals of groups of EIS.

RISK BASED ACCESS CONTROL WITH UNCERTAIN AND TIME-DEPENDENT SENSITIVITY

Topics: Risk Assessment; Access Control in Computing Environments

Authors: John A. Clark ¹ , Juan E. Tapiador ¹ , John McDermid ¹ , Pau-Chen Cheng ² , Dakshi Agrawal ² , Natalie Ivanic ³ and Dave Slogget ⁴

Affiliations: ¹ University of York, United Kingdom , ² IBM Thomas J. Watson Research Center, United States , ³ US Army Research Laboratory, United States , ⁴ LogicaCMG, United Kingdom

Digital Object Identifier: 10.5220/0002935200050013

Proceedings of SECRYPT 2010/ICETE, ISBN: 978-989-8425-18-8

Quick Abstract

In traditional multi-level security (MLS) models, object labels are fixed assessments of sensitivity. In practice there will inevitably be some uncertainty about the damage that might be caused if a document falls into the wrong hands. Furthermore, unless specific management action is taken to regrade the label on an object, it does not change. This does not reflect the operational reality of many modern systems where there is clearly a temporal element to the actual sensitivity of information. Tactical information may be highly sensitive right now but comparatively irrelevant tomorrow whilst strategic secrets may need to be maintained for many years, decades, or even longer. In this paper we propose to model both security labels and clearances as probability distributions. We provide practical templates to model both uncertainty and temporally characterized dependencies, and show how these features can be naturally integrated into a recently proposed access control framework based on quantified risk.

Related Ontology Subjects/Areas/Topics (8)

Access Control	Information and Systems Security	Risk Assessment
Data Engineering	Information Assurance	Web Information Systems and Technologies
Databases and Data Security	Internet Technology

AUTOMATIC MULTILINGUAL LEXICON GENERATION USING WIKIPEDIA AS A RESOURCE

Topics: Machine Learning; Data Mining; Natural Language Processing

Authors: Ahmad R. Shahid and Dimitar Kazakov

Affiliation: The University of York, United Kingdom

Digital Object Identifier: 10.5220/0001783003570360

Proceedings of ICAART 2009, ISBN: 978-989-8111-66-1

Quick Abstract

This paper proposes a method for creating a multilingual dictionary by taking the titles of Wikipedia pages in English and then finding the titles of the corresponding articles in other languages. The creation of such multilingual dictionaries has become possible as a result of exponential increase in the size of multilingual information on the web. Wikipedia is a prime example of such multilingual source of information on any conceivable topic in the world, which is edited by the readers. Here, a web crawler has been used to traverse Wikipedia following the links on a given page. The crawler takes out the title along with the titles of the corresponding pages in other targeted languages. The result is a set of words and phrases that are translations of each other. For efficiency, the URLs are organized using hash tables. A lexicon has been constructed which contains 7-tuples corresponding to 7 different languages, namely: English, German, French, Polish, Bulgarian, Greek and Chinese.

Related Ontology Subjects/Areas/Topics (17)

Applications	Evolutionary Computing	Pattern Recognition
Artificial Intelligence	Knowledge Discovery and Information Retrieval	Sensor Networks
Computational Intelligence	Knowledge Engineering and Ontology Development	Signal Processing
Data Mining	Knowledge-Based Systems	Soft Computing
Databases and Information Systems Integration	Machine Learning	Symbolic Systems
Enterprise Information Systems	Natural Language Processing

EQUATION DISCOVERY FOR MACROECONOMIC MODELLING

Topics: Machine Learning; Industrial Applications of AI

Authors: Dimitar Kazakov ¹ and Tsvetomira Tsenova ²

Affiliations: ¹ The University of York, United Kingdom , ² European Central Bank, Germany

Digital Object Identifier: 10.5220/0001802403180323

Proceedings of ICAART 2009, ISBN: 978-989-8111-66-1

Quick Abstract

This article describes a machine learning based approach applied to acquiring empirical forecasting models. The approach makes use of the LAGRAMGE equation discovery tool to define a potentially very wide range of equations to be considered for the model. Importantly, the equations can vary in the number of terms and types of functors linking the variables. The parameters of each competing equation are automatically fitted to allow the tool to compare the models. The analysts using the tool can exercise their judgement twice, once when defining the equation syntax, restricting in such a way the search to a space known to contain several types of models that are based on theoretical arguments. In addition, one can use the same theoretical arguments to choose among the list of best fitting models, as these can be structurally very different while providing similar fits on the data. Here we describe experiments with macroeconomic data from the Euro area for the period 1971–2007 in which the parameters of hundreds of thousands of structurally different equations are fitted and the equations compared to produce the best models for the individual cases considered. The results show the approach is able to produce complex non-linear models with several equations showing high fidelity.

Related Ontology Subjects/Areas/Topics (9)

Artificial Intelligence	Industrial Applications of AI	Machine Learning
Computational Intelligence	Knowledge Discovery and Information Retrieval	Soft Computing
Evolutionary Computing	Knowledge-Based Systems	Symbolic Systems

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