Enhancing predictive accuracy of cardiac autonomic neuropathy using blood biochemistry features and iterative multitier ensembles

Blood biochemistry attributes form an important class of tests, routinely collected several times per year for many patients with diabetes. The objective of this study is to investigate the role of blood biochemistry for improving the predictive accuracy of the diagnosis of cardiac autonomic neuropathy (CAN) progression. Blood biochemistry contributes to CAN, and so it is a causative factor that can provide additional power for the diagnosis of CAN especially in the absence of a complete set of Ewing tests. We introduce automated iterative multitier ensembles (AIME) and investigate their performance in comparison to base classifiers and standard ensemble classifiers for blood biochemistry attributes. AIME incorporate diverse ensembles into several tiers simultaneously and combine them into one automatically generated integrated system so that one ensemble acts as an integral part of another ensemble. We carried out extensive experimental analysis using large datasets from the diabetes screening research initiative (DiScRi) project. The results of our experiments show that several blood biochemistry attributes can be used to supplement the Ewing battery for the detection of CAN in situations where one or more of the Ewing tests cannot be completed because of the individual difficulties faced by each patient in performing the tests. The results show that AIME provide higher accuracy as a multitier CAN classification paradigm. The best predictive accuracy of 99.57% has been obtained by the AIME combining decorate on top tier with bagging on middle tier based on random forest. Practitioners can use these findings to increase the accuracy of CAN diagnosis.

Large iterative multitier ensemble classifiers for security of big data

This paper introduces and investigates large iterative multitier ensemble (LIME) classifiers specifically tailored for big data. These classifiers are very large, but are quite easy to generate and use. They can be so large that it makes sense to use them only for big data. They are generated automatically as a result of several iterations in applying ensemble meta classifiers. They incorporate diverse ensemble meta classifiers into several tiers simultaneously and combine them into one automatically generated iterative system so that many ensemble meta classifiers function as integral parts of other ensemble meta classifiers at higher tiers. In this paper, we carry out a comprehensive investigation of the performance of LIME classifiers for a problem concerning security of big data. Our experiments compare LIME classifiers with various base classifiers and standard ordinary ensemble meta classifiers. The results obtained demonstrate that LIME classifiers can significantly increase the accuracy of classifications. LIME classifiers performed better than the base classifiers and standard ensemble meta classifiers.

Iterative construction of hierarchical classifiers for phishing website detection

This article is devoted to a new iterative construction of hierarchical classifiers in SimpleCLI for the detection of phishing websites. Our new construction of hierarchical systems creates ensembles of ensembles in SimpleCLI by iteratively linking a top-level ensemble to another middle-level ensemble instead of a base classifier so that the top-level ensemble can generate a large multilevel system. This new construction makes it easy to set up and run such large systems in SimpleCLI. The present article concentrates on the investigation of performance of the iterative construction of such classifiers for the example of detection of phishing websites. We carried out systematic experiments evaluating several essential ensemble techniques as well as more recent approaches and studying their performance as parts of the iterative construction of hierarchical classifiers. The results presented here demonstrate that the iterative construction of hierarchical classifiers performed better than the base classifiers and standard ensembles. This example of application to the classification of phishing websites shows that the new iterative construction combining diverse ensemble techniques into the iterative construction of hierarchical classifiers can be applied to increase the performance in situations where data can be processed on a large computer. © 2014 ACADEMY PUBLISHER.

Typical case behaviour of spin systems in random graph and composite ensembles

This thesis includes analysis of disordered spin ensembles corresponding to Exact Cover, a multi-access channel problem, and composite models combining sparse and dense interactions. The satisfiability problem in Exact Cover is addressed using a statistical analysis of a simple branch and bound algorithm. The algorithm can be formulated in the large system limit as a branching process, for which critical properties can be analysed. Far from the critical point a set of differential equations may be used to model the process, and these are solved by numerical integration and exact bounding methods. The multi-access channel problem is formulated as an equilibrium statistical physics problem for the case of bit transmission on a channel with power control and synchronisation. A sparse code division multiple access method is considered and the optimal detection properties are examined in typical case by use of the replica method, and compared to detection performance achieved by interactive decoding methods. These codes are found to have phenomena closely resembling the well-understood dense codes. The composite model is introduced as an abstraction of canonical sparse and dense disordered spin models. The model includes couplings due to both dense and sparse topologies simultaneously. The new type of codes are shown to outperform sparse and dense codes in some regimes both in optimal performance, and in performance achieved by iterative detection methods in finite systems.

Access to meaningful relationships through virtual instruments and ensembles

This workshop focuses upon research about the qualities of community in music and of music in community facilitated by technologically supported relationships. Generative media systems present an opportunity for users to leverage computational systems to form new relationships through interactive and collaborative experiences. Generative music and art are a relatively new phenomenon that use procedural invention as a creative technique to produce music and visual media. Early systems have demonstrated the potential to provide access to collaborative ensemble experiences for users with little formal musical or artistic expertise. This workshop examines the relational affordances of these systems evidenced by selected field data drawn from the Network Jamming Project. These generative performance systems enable access to unique ensembles with very little musical knowledge or skill and offer the possibility of interactive relationships with artists and musical knowledge through collaborative performance. In this workshop we will focus on data that highlights how these simulated experiences might lead to understandings that may be of social benefit. Conference participants will be invited to jam in real time using virtual interfaces and to evaluate purposively selected video artifacts that demonstrate different kinds of interactive relationship with artists, peers, and community and that enrich the sense of expressive self. Theoretical insights about meaningful engagement drawn from the longitudinal and cross cultural experiences will underpin the discussion and practical presentation.

Music is a Wordless Knowing of Others : Resilience in Virtual Ensembles

Music making affects relationships with self and others by generating a sense of belonging to a culture or ideology (Bamford, 2006; Barovick, 2001; Dillon & Stewart, 2006; Fiske, 2000; Hallam, 2001). Whilst studies from arts education research present compelling examples of these relationships, others argue that they do not present sufficiently validated evidence of a causal link between music making experiences and cognitive or social change (Winner & Cooper, 2000; Winner & Hetland, 2000a, 2000b, 2001). I have suggested elsewhere that this disconnection between compelling evidence and observations of the effects of music making are in part due to the lack of rigor in research and the incapacity of many methods to capture these experiences in meaningful ways (Dillon, 2006). Part of the answer to these questions about rigor and causality lay in the creative use of new media technologies that capture the results of relationships in music artefacts. Crucially, it is the effective management of these artefacts within computer systems that allows researchers and practitioners to collect, organize, analyse and then theorise such music making experiences.

Iterative design within a local community communication fabric

Our research considers the problem designing support for local community communications. We present a description of a suburban community communication fabric as revealed through observations of long-term use of a networked community noticeboard and the introduction of tailored email digest to registered noticeboard users. The paper contributes an understanding of how iterative situated design in a user community can help us to design for participation in the use of technologies that can support growth of a community communication fabric. The different roles of the situated display and email digest are discussed.

A semi-local method for iterative depth-map refinement

We present an iterative hierarchical algorithm for multi-view stereo. The algorithm attempts to utilise as much contextual information as is available to compute highly accurate and robust depth maps. There are three novel aspects to the approach: 1) firstly we incrementally improve the depth fidelity as the algorithm progresses through the image pyramid; 2) secondly we show how to incorporate visual hull information (when available) to constrain depth searches; and 3) we show how to simultaneously enforce the consistency of the depth-map by continual comparison with neighbouring depth-maps. We show that this approach produces highly accurate depth-maps and, since it is essentially a local method, is both extremely fast and simple to implement.

Iterative development of MobileMums : a physical activity intervention for women with young children.

Background To describe the iterative development process and final version of ‘MobileMums’: a physical activity intervention for women with young children (<5 years) delivered primarily via mobile telephone (mHealth) short messaging service (SMS). Methods MobileMums development followed the five steps outlined in the mHealth development and evaluation framework: 1) conceptualization (critique of literature and theory); 2) formative research (focus groups, n= 48); 3) pre-testing (qualitative pilot of intervention components, n= 12); 4) pilot testing (pilot RCT, n= 88); and, 5) qualitative evaluation of the refined intervention (n= 6). Results Key findings identified throughout the development process that shaped the MobileMums program were the need for: behaviour change techniques to be grounded in Social Cognitive Theory; tailored SMS content; two-way SMS interaction; rapport between SMS sender and recipient; an automated software platform to generate and send SMS; and, flexibility in location of a face-to-face delivered component. Conclusions The final version of MobileMums is flexible and adaptive to individual participant’s physical activity goals, expectations and environment. MobileMums is being evaluated in a community-based randomised controlled efficacy trial (ACTRN12611000481976).

A novel iterative method for simulating patient-specific optimal deformation and fit of fracture fixation plates

This paper proposes a new iterative method to achieve an optimally fitting plate for preoperative planning purposes. The proposed method involves integration of four commercially available software tools, Matlab, Rapidform2006, SolidWorks and ANSYS, each performing specific tasks to obtain a plate shape that fits optimally for an individual tibia and is mechanically safe. A typical challenge when crossing multiple platforms is to ensure correct data transfer. We present an example of the implementation of the proposed method to demonstrate successful data transfer between the four platforms and the feasibility of the method.

Iterative intersections

Iterative Intersectioning is a body of art works that comes out of the collaboration between author and electronic artist Jen Seevinck and a community of print artists, most particularly Elizabeth Saunders (EJ) and Robert Oakman. The work shown here is concerned with the creative process of collaboration, specifically as this informs visual forms. This is through our focus on process. This process has facilitated a 'conversational' exchange between all artists and a corresponding evolution in the artworks. In each case the dialogue is either between the author, Jen and EJ or between Jen and Robert. It consists of passing work between parties, interpreting it and working into it, before passing it back. The result is a series of art works including those shown here. The concept evolves in parallel to this. Importantly, at each of her iterations of creative work, the author Jen determines a similar 'treatment' or 'interpretation' across both print artists works at that time. A synthesis of EJ and Robert's creative interpretation -- at a high level -- occurs. In this sense the concept and works can be understood to intersect with one another.

Steady state genuine multipartite entanglement in harmonic oscillator ensembles with a common environment

Steady state entanglement in ensembles of harmonic oscillators with a common squeezed reservoir is studied. Under certain conditions the ensemble features genuine multipartite entanglement in the steady state. Several analytic results regarding the bipartite and multipartite entanglement properties of the system are derived. We also discuss a possible experimental implementation which may exhibit steady state genuine multipartite entanglement.