The narrative approach to personalisation

This article describes the narrative approach to personalisation. This novel approach to the generation of personalised adaptive hypermedia experiences employs runtime reconciliation between a personalisation strategy and a number of contextual models (e.g. user and domain). The approach also advocates the late binding of suitable content and services to the generated personalised pathway resulting in an interactive composition that comprises services as well as content. This article provides a detailed definition of the narrative approach to personalisation and showcases the approach through the examination of two use-cases: the personalised digital educational games developed by the ELEKTRA and 80Days projects; and the personalised learning activities realised as part of the AMAS project. These use-cases highlight the general applicability of the narrative approach and how it has been applied to create a diverse range of real-world systems.     

Propane Reactions over Faujasite Structure Zeolites Type-X and USY: Effect of Zeolite Silica over Alumina Ratio, Strength of Acidity and Kind of Exchanged Metal Ion

In this work we prepared various zeolite-X and USY samples partially exchanged with copper, iron and platinum. These samples were characterized by XRD, Chemical Analysis, SEM-EDS, N₂-adsorption–desorption, ammonia-TPD, and tested as catalysts in high temperature (400 and 550 °C) propane transformation. The obtained results revealed the strong effect of Si/Al ratio in faujasite zeolite structure, the number and strength of acid sites and of the presence of different metal ions in countered ion sites, on the catalytic activity and selectivity of zeolite-X and USY. The highest propane dehydrogenation activity was achieved with the platinum-exchanged X zeolite (∼11.2% propylene yield, ∼31% selectivity). On the contrary USY zeolites showed high cracking capability and relatively low dehydrogenation activity excepting the platinum-exchanged sample which yielded notably high aromatization products.     

Evaluation of commercial brain–computer interfaces in real and virtual world environment: A pilot study

This paper identifies the user’s adaptation on brain-controlled systems and the ability to control brain-generated events in a closed neuro-feedback loop. The user experience is quantified for the further understanding of brain–computer interfacing. A working system has been developed based on off-the-shelf components for controlling a robot in both the real and virtual world. Using commercial brain–computer interfaces (BCIs) the overall cost, set up time and complexity can be reduced. The system is divided in two prototypes based on the headset type used. The first prototype is based on the Neurosky headset and it has been tested with 54 participants in a field study. The second prototype is based on the Emotiv headset including more sensors and accuracy, tested with 31 participants in a lab environment. Evaluation results indicate that robot navigation through commercial BCIs can be effective and natural both in the real and the virtual environment.     

Local and global optimization for Takagi–Sugeno fuzzy system by memetic genetic programming

This work presents a method to incorporate standard neuro-fuzzy learning for Takagi–Sugeno fuzzy systems that evolve under a grammar driven genetic programming (GP) framework. This is made possible by introducing heteroglossia in the functional GP nodes, enabling them to switch behavior according to the selected learning stage. A context-free grammar supports the expression of arbitrarily sized and composed fuzzy systems and guides the evolution. Recursive least squares and backpropagation gradient descent algorithms are used as local search methods. A second generation memetic approach combines the genetic programming with the local search procedures. Based on our experimental results, a discussion is included regarding the competitiveness of the proposed methodology and its properties. The contributions of the paper are: (i) introduction of an approach which enables the application of local search learning for intelligent systems evolved by genetic programming, (ii) presentation of a model for memetic learning of Takagi–Sugeno fuzzy systems, (iii) experimental results evaluating model variants and comparison with state-of-the-art models in benchmarking and real-world problems, (iv) application of the proposed model in control.     

Log File Formats for Parallel Applications: A Review

The objective of this paper is the review of the log file formats that allow the performance visualization of parallel applications based on the usage of message passing interface (MPI) standard. These file formats have been designed by the LANS (Laboratory for Advanced Numerical Software) group of the Argonne National Laboratory and they are distributed together with the corresponding viewers as part of the MPE (multipurpose environment) library of the MPICH implementation of the MPI. The formats studied in this paper is the ALOG, CLOG, SLOG1 and SLOG2 file formats—the formats are studied in chronological order and the main features of their structures are presented.     

Overhead and underground MIMO low voltage broadband over power lines networks and EMI regulations: Towards greener capacity performances

The factors influencing broadband capacity potential of overhead and underground multiple-input multiple-output (MIMO) low-voltage/broadband over power lines (LV/BPL) systems are thoroughly examined. Special attention is given on the capacity effect of today’s well-known power constraints that are imposed to suppress BPL electromagnetic interference to other services (EMI policies).     

EVOLVING NEURAL-SYMBOLIC SYSTEMS GUIDED BY ADAPTIVE TRAINING SCHEMES: APPLICATIONS IN FINANCE

The article presents a hybrid and adaptive intelligent methodology, based on neural logic networks and grammar-guided genetic programming. The aim of the study is to demonstrate how to generate efficient neural logic networks with the aid of genetic programming methods trained adaptively through an innovative scheme. The proposed adaptive training scheme of the genetic programming mechanism leads to the generation of high-diversity solutions and small-sized individuals. The overall methodology is advantageous due to the adaptive training scheme proposed for offering both accurate and interpretable results in the form of expert rules. Moreover, a sensitivity analysis study is provided within the article, comparing the performance of the proposed evolutionary neural logic networks methodology with well-known competitive inductive machine learning approaches. Two financial domains of application have been selected to demonstrate the capabilities of the proposed methodology: (a) classification of credit applicants for consumer loans of a German bank and (b) the credit-scoring decision-making process in an Australian bank. Results seem encouraging since the proposed methodology outperforms a number of competitive existing statistical and intelligent methodologies, while it also produces handy decision rules, short in length and transparent in meaning and use.     

Transport analysis and quality evaluation of MVC video streaming

This paper explores the performance of the emerging H.264/MVC standard for stereo video under various packetization schemes, encoding parameters and network conditions. An experimentation test-bed platform has been developed to support the multi session transmission approach for various video packetization options under various number of NAL units per frame. The paper presents measurements in terms of overhead as well as 3D video quality for sequences with different characteristics in terms of spatial resolution and motion. Extensive test-bed experiments indicate that the fragmentation of frames in more than one NAL units improves the perceived video quality measured objectively in terms of PSNR, VQM, SSIM for both base and non-base view as well as, subjectively in terms of 3D MOS.     

Scalable playback rate control in P2P live streaming systems

Current commercial live video streaming systems are based either on a typical client–server (cloud) or on a peer-to-peer (P2P) architecture. The former architecture is preferred for stability and QoS, provided that the system is not stretched beyond its bandwidth capacity, while the latter is scalable with small bandwidth and management cost. In this paper, we propose a P2P live streaming architecture in which by adapting dynamically the playback rate we guarantee that peers receive the stream even in cases where the total upload bandwidth changes very abruptly. In order to achieve this we develop a scalable mechanism that by probing only a small subset of peers monitors dynamically the total available bandwidth resources and a playback rate control mechanism that dynamically adapts playback rate to the aforementioned resources. We model analytically the relationship between the playback rate and the available bandwidth resources by using difference equations and in this way we are able to apply a control theoretical approach. We also quantify monitoring inaccuracies and dynamic bandwidth changes and we calculate dynamically, as a function of these, the maximum playback rate for which the proposed system able to guarantee the uninterrupted and complete distribution of the stream. Finally, we evaluate the control strategy and the theoretical model in a packet level simulator of a complete P2P live streaming system that we designed in OPNET Modeler. Our evaluation results show the uninterrupted and complete stream delivery (every peer receives more than 99 % of video blocks in every scenario) even in very adverse bandwidth changes.