On the efficiency of user identification: a system-based approach

In the Internet era, users’ fundamental privacy and anonymity rights have received significant research and regulatory attention. This is not only a result of the exponential growth of data that users generate when accomplishing their daily task by means of computing devices with advanced capabilities, but also because of inherent data properties that allow them to be linked with a real or soft identity. Service providers exploit these facts for user monitoring and identification, albeit impacting users’ anonymity, based mainly on personal identifiable information or on sensors that generate unique data to provide personalized services. In this paper, we report on the feasibility of user identification using general system features like memory, CPU and network data, as provided by the underlying operating system. We provide a general framework based on supervised machine learning algorithms both for distinguishing users and informing them about their anonymity exposure. We conduct a series of experiments to collect trial datasets for users’ engagement on a shared computing platform. We evaluate various well-known classifiers in terms of their effectiveness in distinguishing users, and we perform a sensitivity analysis of their configuration setup to discover optimal settings under diverse conditions. Furthermore, we examine the bounds of sampling data to eliminate the chances of user identification and thus promote anonymity. Overall results show that under certain configurations users’ anonymity can be preserved, while in other cases users’ identification can be inferred with high accuracy, without relying on personal identifiable information.     

Closed-loop PLM for intelligent products in the era of the Internet of things

With the advent of the information and related emerging technologies, such as RFID, small size sensors and sensor networks or, more generally, product embedded information devices (PEID), a new generation of products called smart or intelligent products is available in the market.Although various definitions of intelligent products have been proposed, we introduce a new definition of the notion of Intelligent Product inspired by what happens in nature with us as human beings and the way we develop intelligence and knowledge. We see an intelligent product as a product system which contains sensing, memory, data processing, reasoning and communication capabilities at four intelligence levels. This future generations of Intelligent Products will need new Product Data Technologies allowing the seamless interoperability of systems and exchange of not only Static but of Dynamic Product Data as well. Actual standards for PDT cover only lowest intelligence of today’s products. In this context, we try to shape the actual state and a possible future of the Product Data Technologies from a Closed-Loop Product Lifecycle Management (C-L PLM) perspective.Our approach is founded in recent findings of the FP6 IP 507100 project PROMISE and follow-up research work. Standards of the STEP family, covering the product lifecycle to a certain extend (PLCS) as well as MIMOSA and ISO 15926 are discussed together with more recent technologies for the management of ID and sensor data such as EPCglobal, OGC-SWE and relevant PROMISE propositions for standards.Finally, the first efforts towards ontology based semantic standards for product lifecycle management and associated knowledge management and sharing are presented and discussed.     

GDF v2.0, an enhanced version of GDF

An improved version of the function estimation program GDF is presented. The main enhancements of the new version include: multi-output function estimation, capability of defining custom functions in the grammar and selection of the error function. The new version has been evaluated on a series of classification and regression datasets, that are widely used for the evaluation of such methods. It is compared to two known neural networks and outperforms them in 5 (out of 10) datasets.

Program summary

Title of program: GDF v2.0Catalogue identifier: ADXC_v2_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADXC_v2_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 98 147No. of bytes in distributed program, including test data, etc.: 2 040 684Distribution format: tar.gzProgramming language: GNU C++Computer: The program is designed to be portable in all systems running the GNU C++ compilerOperating system: Linux, Solaris, FreeBSDRAM: 200000 bytesClassification: 4.9Does the new version supersede the previous version?: YesNature of problem: The technique of function estimation tries to discover from a series of input data a functional form that best describes them. This can be performed with the use of parametric models, whose parameters can adapt according to the input data.Solution method: Functional forms are being created by genetic programming which are approximations for the symbolic regression problem.Reasons for new version: The GDF package was extended in order to be more flexible and user customizable than the old package. The user can extend the package by defining his own error functions and he can extend the grammar of the package by adding new functions to the function repertoire. Also, the new version can perform function estimation of multi-output functions and it can be used for classification problems.Summary of revisions: The following features have been added to the package GDF:

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Multi-output function approximation. The package can now approximate any function . This feature gives also to the package the capability of performing classification and not only regression.

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User defined function can be added to the repertoire of the grammar, extending the regression capabilities of the package. This feature is limited to 3 functions, but easily this number can be increased.

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Capability of selecting the error function. The package offers now to the user apart from the mean square error other error functions such as: mean absolute square error, maximum square error. Also, user defined error functions can be added to the set of error functions.

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More verbose output. The main program displays more information to the user as well as the default values for the parameters. Also, the package gives to the user the capability to define an output file, where the output of the gdf program for the testing set will be stored after the termination of the process.

Additional comments: A technical report describing the revisions, experiments and test runs is packaged with the source code.Running time: Depending on the train data.     

Scalable nonlinear dynamical systems for agent steering and crowd simulation

We present a new methodology for agent modeling that is scalable and efficient. It is based on the integration of nonlinear dynamical systems and kinetic data structures. The method consists of three layers, which together model 3D agent steering, crowds and flocks among moving and static obstacles. The first layer, the local layer employs nonlinear dynamical systems theory to models low-level behaviors. It is fast and efficient, and it does not depend on the total number of agents in the environment. This dynamical systems-based approach also allows us to establish continuous numerical parameters for modifying each agent's behavior. The second layer, a global environment layer consists of a specifically designed kinetic data structure to track efficiently the immediate environment of each agent and know which obstacles/agents are near or visible to the given agent. This layer reduces the complexity in the local layer. In the third layer, a global planning layer, the problem of target tracking is generalized in a way that allows navigation in maze-like terrains, avoidance of local minima and cooperation between agents. We implement this layer based on two approaches that are suitable for different applications: One approach is to track the closest single moving or static target; the second is to use a pre-specified vector field, which may be generated automatically (with harmonic functions, for example) or based on user input to achieve the desired output. We also discuss how hybrid systems concepts for global planning can capitalize on both our layered approach and the continuous, reactive nature of our agent steering.

We demonstrate the power of the approach through a series of experiments simulating single/multiple agents and crowds moving towards moving/static targets in complex environments.     

Feature selection for classification of oscillating time series

We propose a forward sequential feature selection scheme based on k‐means clustering algorithm to derive the feature subset that classifies best the time series data base, according to the criterion of the corrected Rand index. Moreover, we investigate the effect of the standardization scheme on the feature selection and propose a standardization given by the transform to standard Gaussian distribution. Our interest in this work is in classification of oscillating dynamical systems on the basis of measures computed on time series from these systems. The features to be selected are measures of linear and non‐linear analysis of time series, such as auto‐correlation and Lyapunov exponents, as well as oscillation characteristics, such as the mean magnitude of peaks. Simulations on known oscillating deterministic and stochastic systems showed that, for repeated realizations of the same classification task, the proposed feature selection scheme selected very often the same best feature subset, giving high classification accuracy for any standardization. We found that, regardless of the standardization, the highest classification accuracy could be obtained with a small feature subset, containing most frequently an oscillating‐related feature. The same setting was applied to records of epileptic electroencephalogram signals, giving varying results and dependent on the standardization.     

The ramification problem in temporal databases: an approach with conflicting constraints

In this paper we study the ramification problem in the setting of temporal databases. Standard solutions from the literature on reasoning about action are inadequate because they rely on the assumption that fluents persist, and because actions have effects on the next situation only. In this paper we provide a solution to the ramification problem based on an extension of the situation calculus and the work of McCain and Turner. More specifically, we study the case where there are conflicting effects of an action, a particularly complex problem. Also we present a tool which implements the proposed solution.