Minimizing nasty surprises with better informed decision-making in self-adaptive systems

Designers of self-adaptive systems often formulate adaptive design decisions, making unrealistic or myopic assumptions about the system's requirements and environment. The decisions taken during this formulation are crucial for satisfying requirements. In environments which are characterized by uncertainty and dynamism, deviation from these assumptions is the norm and may trigger 'surprises'. Our method allows designers to make explicit links between the possible emergence of surprises, risks and design trade-offs. The method can be used to explore the design decisions for self-adaptive systems and choose among decisions that better fulfil (or rather partially fulfil) non-functional requirements and address their trade-offs. The analysis can also provide designers with valuable input for refining the adaptation decisions to balance, for example, resilience (i.e. Satisfiability of non-functional requirements and their trade-offs) and stability (i.e. Minimizing the frequency of adaptation). The objective is to provide designers of self adaptive systems with a basis for multi-dimensional what-if analysis to revise and improve the understanding of the environment and its effect on non-functional requirements and thereafter decision-making. We have applied the method to a wireless sensor network for flood prediction. The application shows that the method gives rise to questions that were not explicitly asked before at design-time and assists designers in the process of risk-aware, what-if and trade-off analysis.

Reconfiguration of supply chain network:an ISM-based roadmap to performance

Purpose: Short product life cycle and/or mass customization necessitate reconfiguration of operational enablers of supply chain (SC) from time to time in order to harness high levels of performance. The purpose of this paper is to identify the key operational enablers under stochastic environment on which practitioner should focus while reconfiguring a SC network. Design/methodology/approach: The paper used interpretive structural modeling (ISM) approach that presents a hierarchy-based model and the mutual relationships among the enablers. The contextual relationship needed for developing structural self-interaction matrix (SSIM) among various enablers is realized by conducting experiments through simulation of a hypothetical SC network. Findings: The research identifies various operational enablers having a high driving power towards assumed performance measures. In this regard, these enablers require maximum attention and of strategic importance while reconfiguring SC. Practical implications: ISM provides a useful tool to the SC managers to strategically adopt and focus on the key enablers which have comparatively greater potential in enhancing the SC performance under given operational settings. Originality/value: The present research realizes the importance of SC flexibility under the premise of reconfiguration of the operational units in order to harness high value of SC performance. Given the resulting digraph through ISM, the decision maker can focus the key enablers for effective reconfiguration. The study is one of the first efforts that develop contextual relations among operational enablers for SSIM matrix through integration of discrete event simulation to ISM. © Emerald Group Publishing Limited.

Consumer engagement with self-expressive brands:brand love and WOM outcomes

Purpose: The purpose of this paper is to explore attitudes of consumers who engage with brands through Facebook "likes". It explores the extent to which these brands are self-expressive and examines the relationship between brand "liking" and brand outcomes. Brand outcomes include brand love and advocacy, where advocacy incorporates WOM and brand acceptance. Design/methodology/approach: Findings are presented from a survey of Facebook users who engage with a brand by "liking" it. Findings: Brands "liked" are expressive of the inner or social self. The study identifies a positive relationship between the self-expressive nature of brands "liked" and brand love. Consumers who engage with inner self-expressive brands are more likely to offer WOM for that brand. By contrast, consumers who engage with socially self-expressive brands are more likely to accept wrongdoing from a brand. Research limitations/implications: The research is exploratory and is limited to consumers who are engaged with a brand through "liking" it on the Facebook social network. Practical implications: The study offers suggestions for managers seeking to enhance brand engagement through Facebook "liking", and to encourage positive brand outcomes (such as WOM) among consumers already engaged with a brand on Facebook. Originality/value: This paper provides new insights into consumer brand engagement evidenced through Facebook "liking". It charts the relationship between "liked" self-expressive brands and brand love. Distinctions are drawn between brand outcomes among consumers who "like" for socially self-expressive reasons, and consumers who are brand engaged by "liking" to express their inner selves. © Emerald Group Publishing Limited.

CamSim:a distributed smart camera network simulator

Smart cameras allow pre-processing of video data on the camera instead of sending it to a remote server for further analysis. Having a network of smart cameras allows various vision tasks to be processed in a distributed fashion. While cameras may have different tasks, we concentrate on distributed tracking in smart camera networks. This application introduces various highly interesting problems. Firstly, how can conflicting goals be satisfied such as cameras in the network try to track objects while also trying to keep communication overhead low? Secondly, how can cameras in the network self adapt in response to the behavior of objects and changes in scenarios, to ensure continued efficient performance? Thirdly, how can cameras organise themselves to improve the overall network's performance and efficiency? This paper presents a simulation environment, called CamSim, allowing distributed self-adaptation and self-organisation algorithms to be tested, without setting up a physical smart camera network. The simulation tool is written in Java and hence allows high portability between different operating systems. Relaxing various problems of computer vision and network communication enables a focus on implementing and testing new self-adaptation and self-organisation algorithms for cameras to use.

Some new Results for Additive Self-Dual Codes over GF(4)

* Supported by COMBSTRU Research Training Network HPRN-CT-2002-00278 and the Bulgarian National Science Foundation under Grant MM-1304/03.

String Measure Applied to String Self-Organizing Maps and Networks of Evolutionary Processors

* Supported by projects CCG08-UAM TIC-4425-2009 and TEC2007-68065-C03-02

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.

WDM sensor network approach:bridging the gap towards POF-based photonic sensing

In this work a self-referenced technique for fiberoptic intensity sensors using virtual lock-in amplifiers is proposed and discussed. The topology is compatible with WDM networks so multiple remote sensors can simultaneously be interrogated. A hybrid approach combining both silica fiber Bragg gratings and polymer optical fiber Bragg gratings is analyzed. The feasibility of the proposed solution for potential medical environments and biomedical applications is shown and tested using a selfreferenced configuration based on a power ratio parameter.

Self-aware and self-expressive camera networks

Smart cameras perform on-board image analysis, adapt their algorithms to changes in their environment, and collaborate with other networked cameras to analyze the dynamic behavior of objects. A proposed computational framework adopts the concepts of self-awareness and self-expression to more efficiently manage the complex tradeoffs among performance, flexibility, resources, and reliability. The Web extra at http://youtu.be/NKe31-OKLz4 is a video demonstrating CamSim, a smart camera simulation tool, enables users to test self-adaptive and self-organizing smart-camera techniques without deploying a smart-camera network.

Self-monitoring of blood glucose in Black Caribbean and South Asian Canadians with non-insulin treated Type 2 diabetes mellitus:a qualitative study of patients' perspectives

Background: To examine the views and current practice of SMBG among Black Caribbean and South Asian individuals with non-insulin treated Type 2 diabetes mellitus. Methods: Twelve participants completed semi-structured interviews that were guided by the Health Belief Model and analyzed using thematic network analysis. Results: The frequency of monitoring among participants varied from several times a day to once per week. Most participants expressed similar experiences regarding their views and practices of SMBG. Minor differences across gender and culture were observed. All participants understood the benefits, but not all viewed SMBG as beneficial to their personal diabetes management. SMBG can facilitate a better understanding and maintenance of self-care behaviours. However, it can trigger both positive and negative emotional responses, such as a sense of disappointment when high readings are not anticipated, resulting in emotional distress. Health care professionals play a key role in the way SMBG is perceived and used by patients. Conclusion: While the majority of participants value SMBG as a self-management tool, barriers exist that impede its practice, particularly its cost. How individuals cope with these barriers is integral to understanding why some patients adopt SMBG more than others. © 2013 Gucciardi et al.; licensee BioMed Central Ltd.

Self-adapting parallel metric-space search engine for variable query loads

This research focuses on automatically adapting a search engine size in response to fluctuations in query workload. Deploying a search engine in an Infrastructure as a Service (IaaS) cloud facilitates allocating or deallocating computer resources to or from the engine. Our solution is to contribute an adaptive search engine that will repeatedly re-evaluate its load and, when appropriate, switch over to a dierent number of active processors. We focus on three aspects and break them out into three sub-problems as follows: Continually determining the Number of Processors (CNP), New Grouping Problem (NGP) and Regrouping Order Problem (ROP). CNP means that (in the light of the changes in the query workload in the search engine) there is a problem of determining the ideal number of processors p active at any given time to use in the search engine and we call this problem CNP. NGP happens when changes in the number of processors are determined and it must also be determined which groups of search data will be distributed across the processors. ROP is how to redistribute this data onto processors while keeping the engine responsive and while also minimising the switchover time and the incurred network load. We propose solutions for these sub-problems. For NGP we propose an algorithm for incrementally adjusting the index to t the varying number of virtual machines. For ROP we present an ecient method for redistributing data among processors while keeping the search engine responsive. Regarding the solution for CNP, we propose an algorithm determining the new size of the search engine by re-evaluating its load. We tested the solution performance using a custom-build prototype search engine deployed in the Amazon EC2 cloud. Our experiments show that when we compare our NGP solution with computing the index from scratch, the incremental algorithm speeds up the index computation 2{10 times while maintaining a similar search performance. The chosen redistribution method is 25% to 50% faster than other methods and reduces the network load around by 30%. For CNP we present a deterministic algorithm that shows a good ability to determine a new size of search engine. When combined, these algorithms give an adapting algorithm that is able to adjust the search engine size with a variable workload.

Multiple roles for executive control in belief-desire reasoning:distinct neural networks are recruited for self perspective inhibition and complexity of reasoning

Belief-desire reasoning is a core component of 'Theory of Mind' (ToM), which can be used to explain and predict the behaviour of agents. Neuroimaging studies reliably identify a network of brain regions comprising a 'standard' network for ToM, including temporoparietal junction and medial prefrontal cortex. Whilst considerable experimental evidence suggests that executive control (EC) may support a functioning ToM, co-ordination of neural systems for ToM and EC is poorly understood. We report here use of a novel task in which psychologically relevant ToM parameters (true versus false belief; approach versus avoidance desire) were manipulated orthogonally. The valence of these parameters not only modulated brain activity in the 'standard' ToM network but also in EC regions. Varying the valence of both beliefs and desires recruits anterior cingulate cortex, suggesting a shared inhibitory component associated with negatively valenced mental state concepts. Varying the valence of beliefs additionally draws on ventrolateral prefrontal cortex, reflecting the need to inhibit self perspective. These data provide the first evidence that separate functional and neural systems for EC may be recruited in the service of different aspects of ToM.

Stabilization of self-assembled alumina mesophases

An efficient route to stabilize alumina mesophases derived from evaporation-induced self-assembly is reported after investigating various aspects in-depth: influence of the solvent (EtOH, s-BuOH, and t-BuOH) on the textural and structural properties of the mesophases based on aluminum tri-sec-butoxide (ATSB), synthesis reproducibility, role of nonvolatile acids, and the crystallization and thermal stability of the crystalline counterparts. Mesophase specific surface area and pore uniformity depend notably on the solvent; s-BuOH yields the highest surface area and pore uniformity. The optimal mesophase synthesis is reproducible with standard deviations in the textural parameters below 5%. The most pore-uniform mesophases from the three solvents were thermally activated at 1023 K to crystallize them into γ-alumina. The s-BuOH mesophase is remarkably thermally stable, retaining the mesoscopic wormhole order with 300 m2/g (0.45 cm3/g) and an increased acidic site density. These features are not obtained with EtOH or t-BuOH, where agglomerated γ-Al2O3 crystallites are formed with lower surface areas and broader pore size distributions. This was rationalized by the increase of the hydrolysis rate using EtOH and t-BuOH. t-BuOH dehydrates under the synthesis conditions or reacts with HCl, situations that increase the water concentration and rate of hydrolysis. It was found that EtOH exchanges rapidly, producing a highly reactive Al-ethoxide, thus enhancing the hydrolysis rate as well. Particle heterogeneity with random packing of fibrous and wormhole morphologies, attributed to the high hydrolysis rate, was observed for mesophases derived from both solvents. Such a low particle coordination favors coarsening with enlargement of the pore size distribution upon thermal treatment, explaining the lower thermal stability. Controlled hydrolysis and formation of low-polymerized Al species in s-BuOH are possibly responsible for the adequate assembly onto the surfactant. This was verified by the formation of a regular distribution of relatively size-uniform nanoparticles in the mesophase; high particle coordination prevents coarsening, favors densification, and maintains a relatively uniform pore size distribution upon thermal treatment. The acid removal in the evaporation is another key factor to promote network condensation in this route. © 2013 American Chemical Society.

The network structure of Hungarian business clusters

This article investigates the attitudes to inter-firm co-operation in Hungary by analysing a special group of business networks: the business clusters. Following an overview of cluster policy, a wide range of selfproclaimed business clusters are identified. A small elite of these business networks evolves into successful, sustainable innovative business clusters. However, in the majority of cases, these consortia of interfirm co-operation are not based on a mutually satisfactory model, and as a consequence, many clusters do not survive in the longer term. The paper uses the concepts and models of social network theory in order to explain, why and under what circumstances inter-firm co-operation in clusters enhances the competitiveness of the network as a whole, or alternatively, under what circumstances the cluster remains dependent on Government subsidies. The empirical basis of the study is a thorough internet research about the Hungarian cluster movement; a questionnaire based expert survey among managers of clusters and member companies and a set of in-depth interviews among managers of self-proclaimed clusters. The last chapter analyises the applicability of social network theory in the analysis of business networks and a model involving the value chain is recommended.

An investigation of the strategies used by organizations participating in the welfare-to-work network program and correlation of the strategies with the retention data to determine best practices for job retention among former welfare recipients

This ex post facto study (N = 209) examined the relationships between employer job strategies and job retention among organizations participating in Florida welfare-to-work network programs and associated the strategies with job retention data to determine best practices. ^ An internet-based self-report survey battery was administered to a heterogeneous sampling of organizations participating in the Florida welfare-to-work network program. Hypotheses were tested through correlational and hierarchical regression analytic procedures. The partial correlation results linked each of the job retention strategies to job retention. Wages, benefits, training and supervision, communication, job growth, work/life balance, fairness and respect were all significantly related to job retention. Hierarchical regression results indicated that the training and supervision variable was the best predictor of job retention in the regression equation. ^ The size of the organization was also a significant predictor of job retention. Large organizations reported higher job retention rates than small organizations. There was no statistical difference between the types of organizations (profit-making and non-profit) and job retention. The standardized betas ranged from to .26 to .41 in the regression equation. Twenty percent of the variance in job retention was explained by the combination of demographic and job retention strategy predictors, supporting the theoretical, empirical, and practical relevance of understanding the association between employer job strategies and job retention outcomes. Implications for adult education and human resource development theory, research, and practice are highlighted as possible strategic leverage points for creating conditions that facilitate the development of job strategies as a means for improving former welfare workers’ job retention.^