Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Assessment and the self: academic practice and character attributes

A case is made for how, within higher education, we might make use of the relationship that exists between students’ academic practices and outputs, and their character attributes such as open-mindedness, enthusiasm and perseverance. Examples of how academic practices have the capacity to reveal a range of character attributes are discussed, and even though there are very good reasons for believing this potential exists, the need is identified for further research of a kind that would stimulate engagement from students, teachers and academic support staff. Since any generalised, formalised or non-student-led application of these insights to teaching practice would be inappropriate, two points are made about the nature and application of such investigation. First, qualitative methods, and in particular narrative analysis, would be best suited to the complex, ethically sensitive and significantly idiographic nature of the relationship in question. Second, research that generated detailed case studies would also serve as an appropriate means of inspiring this form of reflection in students. This could occur either as a direct result of students engaging with these case studies, or indirectly via increased teacher and learning development staff’s sensitivity to possibilities of these kinds of dialogues occurring. A brief example from my own teaching experience indicates the form and content of the studies that I have in mind

Inferring Best Strategies from the Aggregation of Information from Multiple Agents: The Cultural Approach

Although learning in MAS is described as a collective experience, most of the times its modeling draws solely or mostly on the results of the interaction between the agents. This abruptly contrasts with our everyday experience where learning relies, to a great extent, on a large stock of already codified knowledge rather than on the direct interaction among the agents. If in the course human history this reliance on already codified knowledge had a significant importance, especially since the discovery of writing, during the last decade the size and availability of this stock has increased notably because of the Internet. Even more, humanity has endowed itself with institutions and organizations devoted to fulfill the role of codifying, preserving and diffusing knowledge since its early days. Cultural Algorithms are one of the few cases where the modeling of this process, although in a limited way, has been attempted. However, even in this case, the modeling lacks some of the characteristics that have made it so successful in human populations, notably its frugality in learning only from a rather small subset of the population and a discussion of its dynamics in terms of hypothesis generation and falsification and the relationship between adaptation and discovery. A deep understanding of this process of collective learning, in all its aspects of generalization and re-adoption of this collective and distilled knowledge, together with its diffusion is a key element to understand how human communities function and how a mixed community of humans and electronic agents could effectively learn. And this is more important now than ever because this process has become not only global and available to large populations but also has largely increased its speed. This research aims to contribute to cover this gap, elucidating on the frugality of the mechanism while mapping it in a framework characterized by a variable level of complexity of knowledge. Also seeks to understand the macro dynamics resulting from the micro mechanisms and strategies chosen by the agents. Nevertheless, as any exercise based on modeling, it portrays a stylized description of reality that misses important points and significant aspects of the real behavior. In this case, while we will focus on individual learning and on the process of generalization and ulterior re-use of these generalizations, learning from other agents is notably absent. We believe however, that this choice contributes to make our model easier to understand and easier to expose the causality relationships emerging from our simulation exercises without sacrificing any significant result

Inferring Best Strategies from the Aggregation of Information from Multiple Agents: The Cultural Approach

Although learning in MAS is described as a collective experience, most of the times its modeling draws solely or mostly on the results of the interaction between the agents. This abruptly contrasts with our everyday experience where learning relies, to a great extent, on a large stock of already codified knowledge rather than on the direct interaction among the agents. If in the course human history this reliance on already codified knowledge had a significant importance, especially since the discovery of writing, during the last decade the size and availability of this stock has increased notably because of the Internet. Even more, humanity has endowed itself with institutions and organizations devoted to fulfill the role of codifying, preserving and diffusing knowledge since its early days. Cultural Algorithms are one of the few cases where the modeling of this process, although in a limited way, has been attempted. However, even in this case, the modeling lacks some of the characteristics that have made it so successful in human populations, notably its frugality in learning only from a rather small subset of the population and a discussion of its dynamics in terms of hypothesis generation and falsification and the relationship between adaptation and discovery. A deep understanding of this process of collective learning, in all its aspects of generalization and re-adoption of this collective and distilled knowledge, together with its diffusion is a key element to understand how human communities function and how a mixed community of humans and electronic agents could effectively learn. And this is more important now than ever because this process has become not only global and available to large populations but also has largely increased its speed. This research aims to contribute to cover this gap, elucidating on the frugality of the mechanism while mapping it in a framework characterized by a variable level of complexity of knowledge. Also seeks to understand the macro dynamics resulting from the micro mechanisms and strategies chosen by the agents. Nevertheless, as any exercise based on modeling, it portrays a stylized description of reality that misses important points and significant aspects of the real behavior. In this case, while we will focus on individual learning and on the process of generalization and ulterior re-use of these generalizations, learning from other agents is notably absent. We believe however, that this choice contributes to make our model easier to understand and easier to expose the causality relationships emerging from our simulation exercises without sacrificing any significant result

The Band Model: contextualising Middle and Upper Palaeolithic sociality within a fission-fusion framework

Since William King’s first description of the species Homo neanderthalensis (1864), assessments of Neanderthal social behaviour have been biased by the assumption that this was a species of simian brutes. However, in recent years, genetic, palaeoanthropological, and archaeological findings have significantly undermined the assumption of specific biological and behavioural differences between Neanderthals and AMHs (Green et al. 2010; Reich et al. 2010; Hammer et al 2011; Mendez et al. 2013; Trinkaus 2011; Zilhão et al. 2010; Henry et al. 2011; Pike et al. 2012; Peresani et al. 2013; Rodriguez-Vidal et al. 2014). Despite these findings, trait-list arguments still dominate research paradigms concerning the sociobehavioural capacities of Neanderthals and AMHs. The current state of the human material, paleontological, and paleogenetic records necessitate a more robust theoretical foundation than the one that trait-list models provide (Barton et al. 2011). A socio-ecological approach based within fission-fusion studies can provide robust test hypotheses with the potential to elucidate the evolution of modern social complexity. Following this direction, this thesis adapts the band model of hunter-gatherer sociality (Layton and O’Hara 2010; Layton et al. 2012) to archaeological investigation. The results of this approach both demonstrate the applicability of the band model to Palaeolithic research and highly suggest that Neanderthals and anatomically modern humans shared a comparable fission-fusion sociality

Nests, arcs and cycles in the lifespan of a studio project

Middlewood Sessions produced a kind of popular music that infuses the timbral aesthetics of jazz and orchestral music with the driving rhythms of dance music. This studio project, lasting for almost eight years, provided a rich resource for gaining insight into the increasingly prevalent context of the domestic project studio via a longitudinal case study approach. At the heart of this research is the desire to understand how people collaborate as part of a studio project, how people use technologies to make music and how all of this unfolds over time. To tackle the question of how to understand the shattered, scattered nature of creative practices, and in extending existing creativity research, I propose three ways of thinking about time: nests, arcs and cycles. While explicating this theoretical framework, something of the specific and idiographic nature of the case study, as an example of contemporary music production, is recounted

Semiotics as the science of memory

The notion of culture implies the relative stability of sets of algorithms that become entrenched in human brains as children become socialized, and, to a lesser extent, when immigrants become assimilated into a new society. The semiotics of culture has used the notion of signs and systems of signs to conceptualize this process, which takes for granted memory as a natural affordance of the brain without raising the question of how and why cultural signs impact behaviour in a durable manner. Indeed, under the influence of structuralism, the semiotics of culture has mostly achieved synchronic descriptions. Dynamic models have been proposed to account for the action of signs (e.g., semiosis, dialogism, dialectic) and their resulting cultural changes and cultural diversity. However, these models have remained remarkably abstract, and somewhat disconnected from the actual brain processes, which must be assumed to be involved in the emergence, maintenance, and transformations of cultures. Semiotic terminology has contributed to a systematic representation of cultural objects and processes but the philosophical origin of its basic concepts has made it difficult to construct a productive interface with the cognitive neurosciences as they have developed and achieved notable advances in the understanding of memory over the last few decades. The purpose of this paper is to suggest that further advances in semiotics will require a shift from philosophical and linguistic notions toward biological and evolutionary models

IVOA Recommendation: Simple Spectral Access Protocol Version 1.1

The Simple Spectral Access (SSA) Protocol (SSAP) defines a uniform interface to remotely discover and access one dimensional spectra. SSA is a member of an integrated family of data access interfaces altogether comprising the Data Access Layer (DAL) of the IVOA. SSA is based on a more general data model capable of describing most tabular spectrophotometric data, including time series and spectral energy distributions (SEDs) as well as 1-D spectra; however the scope of the SSA interface as specified in this document is limited to simple 1-D spectra, including simple aggregations of 1-D spectra. The form of the SSA interface is simple: clients first query the global resource registry to find services of interest and then issue a data discovery query to selected services to determine what relevant data is available from each service; the candidate datasets available are described uniformly in a VOTable format document which is returned in response to the query. Finally, the client may retrieve selected datasets for analysis. Spectrum datasets returned by an SSA spectrum service may be either precomputed, archival datasets, or they may be virtual data which is computed on the fly to respond to a client request. Spectrum datasets may conform to a standard data model defined by SSA, or may be native spectra with custom project-defined content. Spectra may be returned in any of a number of standard data formats. Spectral data is generally stored externally to the VO in a format specific to each spectral data collection; currently there is no standard way to represent astronomical spectra, and virtually every project does it differently. Hence spectra may be actively mediated to the standard SSA-defined data model at access time by the service, so that client analysis programs do not have to be familiar with the idiosyncratic details of each data collection to be accessed

From chance to choice : the development of teachers in a postmodern world.

SIGLEAvailable from British Library Document Supply Centre- DSC:DX185932 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Towards an artefact's-eye view: Non-site analysis of discard patterns and lithic technology in Neotropical settings with a case from Misiones province, Argentina

Surface scatters are an important source of archaeological data in the Neotropics, yet despite their role in exploring regional land use, existing frameworks have serious methodological and theoretical drawbacks. This study proposes a robust alternative to site-centric approaches, by examining spatial and technological variability in time-averaged deposits of artefacts collected from the modern surface of Misiones province, north-eastern Argentina. A family of spatial statistical techniques supported by Monte Carlo simulation identify statistically significant inhomogeneity and clustering in lithic point pattern data. This highlights interaction between technologically meaningful sub-samples of four assemblages, which is interpreted as reflecting long-term discard and association of distinctive reduction sequences. These are irreducible to individual episodes, demonstrating that partitioning palimpsests into sites poorly reflects record formation on a landscape level. This illustrates how explicit models of depositional trends can provide information on indigenous land use, and underlines the rich informative potential of surface archaeology in tropical settings