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.     

Extracting spatiotemporal human activity patterns in assisted living using a home sensor network

This paper presents an automated methodology for extracting the spatiotemporal activity model of a person using a wireless sensor network deployed inside a home. The sensor network is modeled as a source of spatiotemporal symbols whose output is triggered by the monitored person’s motion over space and time. Using this stream of symbols, the problem of human activity modeling is formulated as a spatiotemporal pattern-matching problem on top of the sequence of symbolic information the sensor network produces, and is solved using an exhaustive search algorithm. The effectiveness of the proposed methodology is demonstrated on a real 30-day dataset extracted from an ongoing deployment of a sensor network inside a home monitoring an elder. The developed algorithm examines the person’s data over these 30 days and automatically extracts the person’s daily pattern.     

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.     

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.     

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.     

Temporal and spatial changes in bacterial diversity in mixed use watersheds of the Great Lakes region

Environmental water monitoring is an important responsibility of municipal governments. In this study, we partnered with several municipalities in an extensive sampling program to investigate the effects of spatiotemporal and environmental factors on bacterial diversity in a complex watershed ecosystem containing specific environments including creeks, a river, canals, stormwater outfalls and freshwater lakes of the Niagara Peninsula. Samples were collected using standard municipal protocols and bacterial DNA extracted from these samples was sequenced using high-throughput DNA sequencing targeting the V3–V4 regions of the 16S rRNA gene. Average taxonomic richness and alpha diversity differed significantly between samples collected from lakes and creeks (P?<?0.05), and between lakes and stormwater outfalls (P?<?0.05). Beta diversity also differed significantly (P?<?0.0001) between habitats suggesting that each of these habitats harbours distinct bacterial groups. Among the environmental factors examined, dissolved oxygen (DO) level was strongly associated (P?<?0.001) with bacterial diversity. Using a Bayesian source tracking method, the proportional contribution of creeks, river, canals and stormwater outfall habitats in shaping lake bacterial community structure was quantified. Sequences associated with genera known to contain pathogens as well as fecal indicator bacteria were found in every habitat. This study demonstrates that DNA sequence analysis can augment traditional methods of watershed monitoring and management by providing additional information on bacteria of interest to water quality policy makers. Future work may integrate taxonomic and functional analyses to obtain a greater understanding of pathogen survival, nutrient cycling and microbial interactions in freshwater ecosystems.     

Function matching between binary executables: efficient algorithms and features

Journal of Computer Virology and Hacking Techniques - Binary diffing consists in comparing syntactic and semantic differences of two programs in binary form, when source code is unavailable. It can...     

Silver Nanoparticles and Graphitic Carbon Through Thermal Decomposition of a Silver/Acetylenedicarboxylic Salt

Spherically shaped silver nanoparticles embedded in a carbon matrix were synthesized by thermal decomposition of a Ag(I)/acetylenedicarboxylic acid salt. The silver nanoparticles, which are formed either by pyrolysis at 300 °C in an autoclave or thermolysis in xylene suspension at reflux temperature, are acting catalytically for the formation of graphite layers. Both reactions proceed through in situ reduction of the silver cations and polymerization of the central acetylene triple bonds and the exact temperature of the reaction can be monitored through DTA analysis. Interestingly, the thermal decomposition of this silver salt in xylene partly leads to a minor fraction of quasicrystalline silver, as established by HR-TEM analysis. The graphitic layers covering the silver nanoparticles are clearly seen in HR-TEM images and, furthermore, established by the presence of sp² carbon at the Raman spectrum of both samples.     

Theoretical Perspectives and Empirical Facts on Water Sector Privatization: The Greek Case Against European and Global Trends

The scope of this paper is to investigate the Greek ‘path’ to water privatization and its possible interconnections with the ongoing restructuring of the water sector on an EU and a global level. The paper starts with a contemporary, spatially-sensitive analysis of the expanding role of water multinationals, by focusing on water supply operators of the southern EU. Afterwards, it highlights the peculiarities of the ‘Greek path’ to private sector participation by studying the two most important Greek water companies, in the cities of Athens and Thessaloniki respectively. As found, these companies have partially, though successfully, been transformed according to the rules of the ‘market-environmentalist’ paradigm. The state drives the privatization effort, while at the same time, insufficiently regulates the activity of both companies. Based on the Greek case, the paper discusses whether the analytical tools offered by a critical approach, the ‘accumulation-by-dispossession’ thesis, can better interpret changes in water companies of the ‘advanced-South’.