A simulation model of self-organising evolvability in software systems

The evolvability of a software artifact is its capacity for producing heritable or reusable variants; the inverse quality is the artifact's inertia or resistance to evolutionary change. Evolvability in software systems may arise from engineering and/or self-organising processes. We describe our 'Conditional Growth' simulation model of software evolution and show how, it can be used to investigate evolvability from a self-organisation perspective. The model is derived from the Bak-Sneppen family of 'self-organised criticality' simulations. It shows good qualitative agreement with Lehman's 'laws of software evolution' and reproduces phenomena that have been observed empirically. The model suggests interesting predictions about the dynamics of evolvability and implies that much of the observed variability in software evolution can be accounted for by comparatively simple self-organising processes.

Self-organising coordination systems

Many research fields are pushing the engineering of large-scale, mobile, and open systems towards the adoption of techniques inspired by self-organisation: pervasive computing, but also distributed artificial intelligence, multi-agent systems, social networks, peer-topeer and grid architectures exploit adaptive techniques to make global system properties emerge in spite of the unpredictability of interactions and behaviour. Such a trend is visible also in coordination models and languages, whenever a coordination infrastructure needs to cope with managing interactions in highly dynamic and unpredictable environments. As a consequence, self-organisation can be regarded as a feasible metaphor to define a radically new conceptual coordination framework. The resulting framework defines a novel coordination paradigm, called self-organising coordination, based on the idea of spreading coordination media over the network, and charge them with services to manage interactions based on local criteria, resulting in the emergence of desired and fruitful global coordination properties of the system. Features like topology, locality, time-reactiveness, and stochastic behaviour play a key role in both the definition of such a conceptual framework and the consequent development of self-organising coordination services. According to this framework, the thesis presents several self-organising coordination techniques developed during the PhD course, mainly concerning data distribution in tuplespace-based coordination systems. Some of these techniques have been also implemented in ReSpecT, a coordination language for tuple spaces, based on logic tuples and reactions to events occurring in a tuple space. In addition, the key role played by simulation and formal verification has been investigated, leading to analysing how automatic verification techniques like probabilistic model checking can be exploited in order to formally prove the emergence of desired behaviours when dealing with coordination approaches based on self-organisation. To this end, a concrete case study is presented and discussed.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Self-organising neural networks for adaptive control

Self-organizing neural networks have been implemented in a wide range of application areas such as speech processing, image processing, optimization and robotics. Recent variations to the basic model proposed by the authors enable it to order state space using a subset of the input vector and to apply a local adaptation procedure that does not rely on a predefined test duration limit. Both these variations have been incorporated into a new feature map architecture that forms an integral part of an Hybrid Learning System (HLS) based on a genetic-based classifier system. Problems are represented within HLS as objects characterized by environmental features. Objects controlled by the system have preset targets set against a subset of their features. The system's objective is to achieve these targets by evolving a behavioural repertoire that efficiently explores and exploits the problem environment. Feature maps encode two types of knowledge within HLS — long-term memory traces of useful regularities within the environment and the classifier performance data calibrated against an object's feature states and targets. Self-organization of these networks constitutes non-genetic-based (experience-driven) learning within HLS. This paper presents a description of the HLS architecture and an analysis of the modified feature map implementing associative memory. Initial results are presented that demonstrate the behaviour of the system on a simple control task.

BioTuCSoN: biochemical extension of TuCSoN to support self-organising coordination

Starting from pervasive computing paradigm, we want to face the new system's requirements, concerning, mainly, self-organisation, situatedness and adaptivity, through the definition and execution of nature-inspired patterns. They are extracted by the study of dynamics in biological systems and we consider for their implementation the biochemical tuple spaces model. In particular, the aim of the thesis is to design and realize a first biochemical extension of TuCSoN (technology based on tuple spaces model) and, then, to verify its capabilities by means of a proper case study, that deals with local self-organisation and competition of services in an open and highly-dynamic environment.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.

Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach

Financial prediction has attracted a lot of interest due to the financial implications that the accurate prediction of financial markets can have. A variety of data driven modellingapproaches have been applied but their performance has produced mixed results. In this study we apply both parametric (neural networks with active neurons) and nonparametric (analog complexing) self-organisingmodelling methods for the daily prediction of the exchangerate market. We also propose acombinedapproach where the parametric and nonparametricself-organising methods are combined sequentially, exploiting the advantages of the individual methods with the aim of improving their performance. The combined method is found to produce promising results and to outperform the individual methods when tested with two exchangerates: the American Dollar and the Deutche Mark against the British Pound.

Nootropia : a user profiling model based on a self-organising term network

Artificial Immune Systems are well suited to the problem of using a profile representation of an individual’s or a group’s interests to evaluate documents. Nootropia is a user profiling model that exhibits similarities to models of the immune system that have been developed in the context of autopoietic theory. It uses a self-organising term network that can represent a user’s multiple interests and can adapt to both short-term variations and substantial changes in them. This allows Nootropia to drift, constantly following changes in the user’s multiple interests, and, thus, to become structurally coupled to the user.

Self-organising zooms for decentralised redundancy management in visual sensor networks

When visual sensor networks are composed of cameras which can adjust the zoom factor of their own lens, one must determine the optimal zoom levels for the cameras, for a given task. This gives rise to an important trade-off between the overlap of the different cameras’ fields of view, providing redundancy, and image quality. In an object tracking task, having multiple cameras observe the same area allows for quicker recovery, when a camera fails. In contrast having narrow zooms allow for a higher pixel count on regions of interest, leading to increased tracking confidence. In this paper we propose an approach for the self-organisation of redundancy in a distributed visual sensor network, based on decentralised multi-objective online learning using only local information to approximate the global state. We explore the impact of different zoom levels on these trade-offs, when tasking omnidirectional cameras, having perfect 360-degree view, with keeping track of a varying number of moving objects. We further show how employing decentralised reinforcement learning enables zoom configurations to be achieved dynamically at runtime according to an operator’s preference for maximising either the proportion of objects tracked, confidence associated with tracking, or redundancy in expectation of camera failure. We show that explicitly taking account of the level of overlap, even based only on local knowledge, improves resilience when cameras fail. Our results illustrate the trade-off between maintaining high confidence and object coverage, and maintaining redundancy, in anticipation of future failure. Our approach provides a fully tunable decentralised method for the self-organisation of redundancy in a changing environment, according to an operator’s preferences.

A Microleakage Study Of Gutta-percha/ah Plus And Resilon/real Self-etch Systems After Different Irrigation Protocols.

The development and maintenance of the sealing of the root canal system is the key to the success of root canal treatment. The resin-based adhesive material has the potential to reduce the microleakage of the root canal because of its adhesive properties and penetration into dentinal walls. Moreover, the irrigation protocols may have an influence on the adhesiveness of resin-based sealers to root dentin. The objective of the present study was to evaluate the effect of different irrigant protocols on coronal bacterial microleakage of gutta-percha/AH Plus and Resilon/Real Seal Self-etch systems. One hundred ninety pre-molars were used. The teeth were divided into 18 experimental groups according to the irrigation protocols and filling materials used. The protocols used were: distilled water; sodium hypochlorite (NaOCl)+eDTA; NaOCl+H3PO4; NaOCl+eDTA+chlorhexidine (CHX); NaOCl+H3PO4+CHX; CHX+eDTA; CHX+ H3PO4; CHX+eDTA+CHX and CHX+H3PO4+CHX. Gutta-percha/AH Plus or Resilon/Real Seal Se were used as root-filling materials. The coronal microleakage was evaluated for 90 days against Enterococcus faecalis. Data were statistically analyzed using Kaplan-Meier survival test, Kruskal-Wallis and Mann-Whitney tests. No significant difference was verified in the groups using chlorhexidine or sodium hypochlorite during the chemo-mechanical preparation followed by eDTA or phosphoric acid for smear layer removal. The same results were found for filling materials. However, the statistical analyses revealed that a final flush with 2% chlorhexidine reduced significantly the coronal microleakage. A final flush with 2% chlorhexidine after smear layer removal reduces coronal microleakage of teeth filled with gutta-percha/AH Plus or Resilon/Real Seal SE.

Probing Individual Quantum Dots: Noise in Self-Assembled Systems

In this work we explore the noise characteristics in lithographically-defined two terminal devices containing self-assembled InAs/InP quantum dots. The experimental ensemble of InAs dots show random telegraph noise (RTN) with tuneable relative amplitude-up to 150%-in well defined temperature and source-drain applied voltage ranges. Our numerical simulation indicates that the RTN signature correlates with a very low number of quantum dots acting as effective charge storage centres in the structure for a given applied voltage. The modulation in relative amplitude variation can thus be associated to the altered electrostatic potential profile around such centres and enhanced carrier scattering provided by a charged dot.

Microshear Bond Strength of Self-etching Systems Associated with a Hydrophobic Resin Layer

Purpose: To evaluate in vitro the microshear bond strength of adhesive systems applied to dentin according to manufacturers` instructions, associated or not with a hydrophobic layer of unfilled resin. Materials and Methods: Six self-etching adhesives (Clearfil SE Bond, Kuraray Medical; AdheSE, lvoclar Vivadent; Xeno III, Dentsply; I Bond, Heraeus-Kulzer; Bond Force, Tokuyama; Futurabond DC, Voco) were tested. The labial dentin of sixty bovine incisors was exposed, and the teeth were divided into two groups according to the application or not of an extra hydrophobic resin layer (Scotchbond Multi Purpose Plus, bottle 3). Six composite cylinders (Filtek Z250, 3M ESPE) were built up on each treated surface. Specimens were stored in distilled water at 37 C for 24 h and then subjected to the microshear bond strength test in a universal testing machine at a crosshead speed of 0.5 mm/min. Microshear bond strength values were analyzed by 2-way ANOVA and Tukey`s post-hoc test. Failure mode was determined using a stereomicroscope under 20X magnification. Results: The application of the hydrophobic resin layer did not affect bond strength, except for AdheSE. However, the bond strengths with the hydrophobic layer were similar among the six tested systems (Clearfil: 17.1 +/- 7.9; AdheSE: 14.5 +/- 7.1; Xeno III: 12.8 +/- 7.7; I Bond: 9.5 +/- 5.8; Bond Force: 17.5 +/- 4.1; Futurabond: 7.7 +/- 2.3). When used as recommended by the manufacturers, Bond Force presented statistically higher bond strength than AdheSE and I Bond (p < 0.05) (Clearfil 10.4 +/- 4.9; AdheSE 1.6 +/- 1.6; Xeno III: 9.0 +/- 3.8; I Bond: 3.0 +/- 1.5; Bond Force: 14 +/- 3.9; Futurabond: 8.8 +/- 3.8). Failure mode was predominantly adhesive. Conclusion: The bond strength of the self-etching systems tested was not significantly affected by the application of a hydrophobic layer, but a significant improvement was observed in AdheSE.

Durability of Enamel Bonding Using One-step Self-etch Systems on Ground and Unground Enamel

Objective: To examine the morphological, early and long-term microtensile bond strengths (mu TBS) of one-step self-etch systems to unground and ground enamel. Materials and Methods: Resin composite (Filtek Z250) buildups were bonded to the buccal and lingual enamel surfaces (unground, bur-cut or SiC-roughened enamel) of third molars after adhesive application using the following adhesives: Clearfil S(3) Bond (CS3); Adper Prompt L-Pop (ADP); iBond (iB) and, as the control, Clearfil SE Bond (CSE). Six tooth halves were assigned for each condition. After storage in water (24 hours/37 degrees C), the bonded specimens were sectioned into beams (0.8 mm(2)) and subjected to pTBS (0.5 mm/min) either immediately (IM) or after six (6M) or 12 months (12M) of water storage. The data were analyzed by three-way repeated measures ANOVA and Tukey`s test (alpha=0.05). Surface conditioning was observed under scanning electron microscopy (SEM). Results: The mu TBS in the Si-C paper and diamond bur groups were similar and higher than the unground group. No significant difference was observed among the different storage periods, except for CS3, which showed an increase in the pTBS after 12M. The etching pattern was more retentive on ground enamel. Conclusions: One-step self-etch adhesives showed higher bond strengths on ground enamel and no reductions in resin-enamel bonds were observed after 12M of water storage.

Durability of enamel bonding using two-step self-etch systems on ground and unground enamel

This study examined the early and long-term microtensile bond strengths (mu TBS) and interfacial enamel gap formation (IGW) of two-step selfetch systems to unground and ground enamel. Resin composite (Filtek Z250) buildups were bonded to proximal enamel surfaces (unground, bur-cut or SiC-treated enamel) of third molars after the application of four self-etch adhesives: a mild (Clearfil SE Bond [SE]), two moderate (Optibond Solo Plus Self-Etch Primer [SO] and AdheSE [AD]) and a strong adhesive (Tyrian Self Priming Etchant + One Step Plus [TY]) and two etch-and-rinse adhesive systems (Single Bond [SB] and Scotchbond Multi-Purpose Plus [SBMP]). Ten tooth halves were assigned for each adhesive. After storage in water (24 hours/37 degrees C), the bonded specimens were sectioned into beams (0.9 mm(2)) and subjected to mu TBS (0.5 mm/minute) or interfacial gap width measurement (stereomicroscope at 400x) either immediately (IM) or after 12 months (12M) of water storage. The data were analyzed by three-way repeated measures ANOVA and Tukey`s test (alpha=0.05). No gap formation was observed in any experimental condition. The mu TBS in the Si-C paper and diamond bur groups were similar and greater than the unground group only for the moderate self-etch systems (SO and AD). No reductions in bond strength values were observed after 12 months of water storage, regardless of the adhesive evaluated.