Knowledge discovery in grammatically analysed corpora

Abstract. Collections of grammatically annotated texts (corpora), and in particular, parsed corpora, present a challenge to current methods of analysis. Such corpora are large and highly structured heterogeneous data sources. In this paper we briefly describe the parsed one-million word ICE-GB corpus, and the ICECUP query system. We then consider the application of knowledge discovery in databases (KDD) to text corpora. Following Cupit and Shadbolt (Proceedings 9th European Knowledge Acquisition Workshop, EKAW '96; Berlin: Springer Verlag, pp. 245-261, 1996), we argue that effective linguistic knowledge discovery must be based on a process of redescription or, more precisely, abstraction, based on the research question to be investigated. Abstraction maps relevant elements from the corpus to an abstract model of the research topic. This mapping may be implemented using a grammatical query representation such as ICECUP's Fuzzy Tree Fragments (FTFs). Since this abstractive process must be both experimental and expert-guided, ultimately a workbench is necessary to maintain, evaluate and refine the abstract model. We conclude with a pilot study, employing our approach, into aspects of noun phrase postmodifying clause structure. The data is analysed using the UNIT machine learning algorithm to search for significant interactions between domain variables. We show that our results are commensurable with those published in the linguistics literature, and discuss how the methodology may be improved

Toward a process theory of entrepreneurship: revisiting opportunity identification and entrepreneurial actions

This dissertation studies the early development of new ventures and small business and the entrepreneurship process from initial ideas to viable ventures. I unpack the micro-foundations of entrepreneurial actions and new ventures’ investor communications through quality signals to finance their growth path. This dissertation includes two qualitative papers and one quantitative study. The qualitative papers employ an inductive multiple-case approach and include seven medical equipment manufacturers (new ventures) in a nascent market context (the mobile health industry) across six U.S. states and a secondary data analysis to understand the emergence of opportunities and the early development of new ventures. The quantitative research chapter includes 770 IPOs in the manufacturing industries in the U.S. and investigates the legitimation strategies of young ventures to gain resources from targeted resource-holders.Open Acces

NASA JSC neural network survey results

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc

Transition expertise: Cognitive factors and developmental processes that contribute to repeated successful career transitions amongst elite athletes, musicians and business people

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis examines the nature of transition expertise which enables individuals to make repeated successful transitions over the course of their career. It addressed four areas that contribute to transition expertise: 1) cognitive flexibility that enables the generalisation of expert knowledge and processes; 2) inferential and inductive cognitive mechanisms that enable expertise to be generalised; 3) personal intelligences that are used to support transitions; and 4) practical intelligence as it supports performance contextually during transitions. The study used retrospective interviews to gather data from elite performers in three fields who had made successful career transitions: sports people who become national coaches or heads of national bodies; successful musicians who become heads of faculty or principals of a conservatoire; successful business people who become senior vice presidents or CEOs. Participants were able to generalise expert knowledge and processes beyond their primary domains, contrary to widely held views about the domain specificity of expertise. Cognitive flexibility enabled this generalisation and was developed through broad based training, early exposure to multiple domains and the early use of generative cognitive processes during the development of primary domain expertise. Inductive, inferential and analogical cognitive mechanisms were the main tools through which expertise was generalised during transitions. Personal intelligence contributed to transition expertise. Intrapersonal intelligence enabled individuals to understand how their abilities, values and motivations shaped their career progression. Interpersonal intelligence enabled individuals to respond effectively to the requirements of their peers, direct reports, stakeholders and organisational context. Contrary to expectations, self regulatory processes did not play a central role in the management of transitions. Practical intelligence enabled transition expertise. It involved more than applying subject-area and tacit knowledge. It encompassed the abilities to: identify and resolve problems; manipulate environmental objects in the form of administrative tasks, schedules and plans; utilise resources in terms of people and materials; and shape their environment, corporate structures and culture. Transition expertise develops and evolves over the course of a career as it uses convergent and divergent cognitive processes, inductive mechanisms, personal awareness and cognitive pragmatics to address issues of increasing scope and implication. While motivational factors, self belief and personality resiliency are important contributors to transition expertise they did not form part of this study

Work-based learning and research for mid-career professionals: professional studies in Australia

Work-based learning has been identified in the literature, and is established in academia and in the global worlds of work; however, an examination of work-based research, particularly at the doctoral level, has been less well articulated. Moreover, a paucity of published literature on either work-based research or Professional Studies means little is known about the dynamics and drivers of these domains. This study aims to begin addressing the shortfall in literature on work-based research and Professional Studies programs, using the program at University of Southern Queensland as an example. This paper examines work-based research in the context of the Professional Studies program at University of Southern Queensland in Australia. Analysis of work-based research includes discussion of ‘messy’ research environments and the changing nature of workplaces, along with the opportunities and challenges such environments pose for action researchers. In addition to addressing a shortfall in the published literature on work-based research, the paper also contributes insight into the mechanisms used to promote reflective practice and the generation of professional artefacts. Often driven by altruism, work-based research as implemented in the Professional Studies program results in a so-called ‘triple dividend’, designed to benefit the individual researcher, work environment, and community of practice

Exploiting multimodality and structure in world representations

An essential aim of artificial intelligence research is to design agents that will eventually cooperate with humans within the real world. To this end, embodied learning is emerging as one of the most important efforts contributed by the machine learning community towards this goal. Recently developing sub-fields concern various aspects of such systems---visual reasoning, language representations, causal mechanisms, robustness to out-of-distribution inputs, to name only a few. In particular, multimodal learning and language grounding are vital to achieving a strong understanding of the real world. Humans build internal representations via interacting with their environment, learning complex associations between visual, auditory and linguistic concepts. Since the world abounds with structure, graph-based encodings are also likely to be incorporated in reasoning and decision-making modules. Furthermore, these relational representations are rather symbolic in nature---providing advantages over other formats, such as raw pixels---and can encode various types of links (temporal, causal, spatial) which can be essential for understanding and acting in the real world. This thesis presents three research works that study and develop likely aspects of future intelligent agents. The first contribution centers on vision-and-language learning, introducing a challenging embodied task that shifts the focus of an existing one to the visual reasoning problem. By extending popular visual question answering (VQA) paradigms, I also designed several models that were evaluated on the novel dataset. This produced initial performance estimates for environment understanding, through the lens of a more challenging VQA downstream task. The second work presents two ways of obtaining hierarchical representations of graph-structured data. These methods either scaled to much larger graphs than the ones processed by the best-performing method at the time, or incorporated theoretical properties via the use of topological data analysis algorithms. Both approaches competed with contemporary state-of-the-art graph classification methods, even outside social domains in the second case, where the inductive bias was PageRank-driven. Finally, the third contribution delves further into relational learning, presenting a probabilistic treatment of graph representations in complex settings such as few-shot, multi-task learning and scarce-labelled data regimes. By adding relational inductive biases to neural processes, the resulting framework can model an entire distribution of functions which generate datasets with structure. This yielded significant performance gains, especially in the aforementioned complex scenarios, with semantically-accurate uncertainty estimates that drastically improved over the neural process baseline. This type of framework may eventually contribute to developing lifelong-learning systems, due to its ability to adapt to novel tasks and distributions. The benchmark, methods and frameworks that I have devised during my doctoral studies suggest important future directions for embodied and graph representation learning research. These areas have increasingly proved their relevance to designing intelligent and collaborative agents, which we may interact with in the near future. By addressing several challenges in this problem space, my contributions therefore take a few steps towards building machine learning systems to be deployed in real-life settings.DREAM CD