From Keyword Search to Exploration: How Result Visualization Aids Discovery on the Web

A key to the Web's success is the power of search. The elegant way in which search results are returned is usually remarkably effective. However, for exploratory search in which users need to learn, discover, and understand novel or complex topics, there is substantial room for improvement. Human computer interaction researchers and web browser designers have developed novel strategies to improve Web search by enabling users to conveniently visualize, manipulate, and organize their Web search results. This monograph offers fresh ways to think about search-related cognitive processes and describes innovative design approaches to browsers and related tools. For instance, while key word search presents users with results for specific information (e.g., what is the capitol of Peru), other methods may let users see and explore the contexts of their requests for information (related or previous work, conflicting information), or the properties that associate groups of information assets (group legal decisions by lead attorney). We also consider the both traditional and novel ways in which these strategies have been evaluated. From our review of cognitive processes, browser design, and evaluations, we reflect on the future opportunities and new paradigms for exploring and interacting with Web search results

A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998

Neuroengineering of Clustering Algorithms

Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of multi-criteria ART models: dual vigilance fuzzy ART and distributed dual vigilance fuzzy ART (both of which are capable of detecting complex cluster structures), merge ART (aggregates partitions and lessens ordering effects in online learning), and cluster validity index vigilance in fuzzy ART (features a robust vigilance parameter selection and alleviates ordering effects in offline learning). The sixth paper consists of enhancements to data visualization using self-organizing maps (SOMs) by depicting in the reduced dimension and topology-preserving SOM grid information-theoretic similarity measures between neighboring neurons. This visualization\u27s parameters are estimated using samples selected via a single-linkage procedure, thereby generating heatmaps that portray more homogeneous within-cluster similarities and crisper between-cluster boundaries. The seventh paper presents incremental cluster validity indices (iCVIs) realized by (a) incorporating existing formulations of online computations for clusters\u27 descriptors, or (b) modifying an existing ART-based model and incrementally updating local density counts between prototypes. Moreover, this last paper provides the first comprehensive comparison of iCVIs in the computational intelligence literature --Abstract, page iv

Formal concept matching and reinforcement learning in adaptive information retrieval

The superiority of the human brain in information retrieval (IR) tasks seems to come firstly from its ability to read and understand the concepts, ideas or meanings central to documents, in order to reason out the usefulness of documents to information needs, and secondly from its ability to learn from experience and be adaptive to the environment. In this work we attempt to incorporate these properties into the development of an IR model to improve document retrieval. We investigate the applicability of concept lattices, which are based on the theory of Formal Concept Analysis (FCA), to the representation of documents. This allows the use of more elegant representation units, as opposed to keywords, in order to better capture concepts/ideas expressed in natural language text. We also investigate the use of a reinforcement leaming strategy to learn and improve document representations, based on the information present in query statements and user relevance feedback. Features or concepts of each document/query, formulated using FCA, are weighted separately with respect to the documents they are in, and organised into separate concept lattices according to a subsumption relation. Furthen-nore, each concept lattice is encoded in a two-layer neural network structure known as a Bidirectional Associative Memory (BAM), for efficient manipulation of the concepts in the lattice representation. This avoids implementation drawbacks faced by other FCA-based approaches. Retrieval of a document for an information need is based on concept matching between concept lattice representations of a document and a query. The learning strategy works by making the similarity of relevant documents stronger and non-relevant documents weaker for each query, depending on the relevance judgements of the users on retrieved documents. Our approach is radically different to existing FCA-based approaches in the following respects: concept formulation; weight assignment to object-attribute pairs; the representation of each document in a separate concept lattice; and encoding concept lattices in BAM structures. Furthermore, in contrast to the traditional relevance feedback mechanism, our learning strategy makes use of relevance feedback information to enhance document representations, thus making the document representations dynamic and adaptive to the user interactions. The results obtained on the CISI, CACM and ASLIB Cranfield collections are presented and compared with published results. In particular, the performance of the system is shown to improve significantly as the system learns from experience.The School of Computing, University of Plymouth, UK

The Musical Structure of Time in the Brain: Repetition, Rhythm, and Harmony in fMRI During Rest and Passive Movie Viewing

© Copyright © 2020 Lloyd. Space generally overshadows time in the construction of theories in cognitive neuroscience. In this paper, we pivot from the spatial axes to the temporal, analyzing fMRI image series to reveal structures in time rather than space. To determine affinities among global brain patterns at different times, core concepts in network analysis (derived from graph theory) were applied temporally, as relations among brain images at every time point during an fMRI scanning epoch. To explore the temporal structures observed through this adaptation of network analysis, data from 180 subjects in the Human Connectome Project were examined, during two experimental conditions: passive movie viewing and rest. The temporal brain, like the spatial brain, exhibits a modular structure, where “modules” are intermittent (distributed in time). These temporal entities are here referred to as themes. Short sequences of themes – motifs – were studied in sequences from 4 to 11 s in length. Many motifs repeated at constant intervals, and are therefore rhythmic; rhythms, converted to frequencies, were often harmonic. We speculate that the structure and interaction of these global oscillations underwrites the capacity to experience and navigate a world which is both recognizably stable and noticeably changing at every moment – a temporal world. In its temporal structure, this brain-constituted world resembles music

System Governance Analysis of Complex Systems

The purpose of this research was to develop and deploy a systems-based framework for analysis of complex governance systems using a multimethodology research design. Two research gaps motivated this research: (1) lack of an integrated conceptualization of a system governance construct, (2) an absence of studies that consider both the governed and governing systems as well as the emergent interactions that arise from within complex governance systems. The research focused on three primary questions: (1) What are the distinctive characteristics of governance?; (2) What system-based framework can be developed for analysis of governance in complex systems?, and (3) What results from deployment of the framework in a field setting? The multimethodology research design that guided the effort included three primary phases. First, the literature was synthesized to derive a set of governance elements. This synthesis was accomplished across an extensive and multidisciplinary literature set by a novel method of content document clustering analysis to reveal important elements of governance. Second, a conceptual framework for analysis of system governance was constructed from the confluence of extant governance literature and systems theory. This governance system analysis framework was informed by Bunge\u27s (2003) system perspective to advance the understanding of governance that will be meaningful in a given practice. Finally, a case based application of the analysis framework was conducted to examine implications of the framework from a field perspective. The original research provided contributions to theory, methodology, and practice. From a theoretical perspective, the research contributed to the body of knowledge by providing: (1) a literature derived set of generalizable elements of governance, and (2) the development of a systems-based framework to be used to analyze complex governance systems. From a methodological stand-point, the research advanced an integrated multimethodology research design that featured: (1) a novel content analysis approach for synthesis of diverse literature; (2) the development of an integrated systems analysis method; and (3) a rigorous single-case study application within the engineering management discipline. Lastly, from a practical perspective, the systems framework provided a foundation for derivative approaches to enhance practices related to system governance

Adapting history: applying adaptation theory to historical film and television

This thesis argues that historical films and television programmes help generate new interpretations of the past, even when they depart from a common interpretation of how history is generally understood. In order to do this a variety of films and television programmes are analysed through the lens of adaptations studies. This thesis presents an analysis of current research in adaptation studies, alongside contemporary research into historical film. Four questions concerning historical adaptation are identified through which an original contribution to existing knowledge is made. These questions are: a)To what extent is an adaptation’s presentation of the past aware of the context of the depicted historical events? b) How can the addition of elements which were not present within the surviving sources, for example anachronisms, function within a historical adaptation? c) How can an adaptation promote a new interpretation of the events which are the focus of the adaptation, as well as how those events relate to the present? d) How can an adaptation be used to inform, critique, or aid in an audience’s understanding of history? The ideas that emerge from a literature review are explored over the course of four separate, but interrelated, case studies. These case studies each focusing on a different aspect of historical adaptation. The results are then combined in the conclusion in order to create a cohesive, central argument about the potential benefits of historical adaptation in film and television

Between Performances, Texts, and Editions: The Changeling

This thesis is about the ways in which Thomas Middleton and William Rowley’s play The Changeling has been edited, performed, and archived in the twentieth and twenty-first centuries. It proposes a more integrated way of looking at the histories of performances and texts than is usually employed by the institutions of Shakespeare and early modern studies. Crucially, it suggests that documented archival remains of performance should be admitted as textual witnesses of a play’s history, and given equal status with academic, scholarly editions. I argue that—despite at least a century of arguments to the contrary—performance is still considered secondary to text, and that this relationship needs to become more balanced, particularly since the canon has begun to expand and early modern plays beyond Shakespeare have begun to see more stage time in recent years. In addition, I begin to theorise social media as archives of performance, and begin to suggest ways forward for archiving the performance of early modern drama in the digital turn. In order to support these arguments, I offer a series of twentieth- and twenty-first-century productions of The Changeling as case studies. Through these case studies, I seek to make connections between The Changeling as text, The Changeling as performance, and the various other texts and performances that it has interacted with throughout its life since 1961. In presenting analyses of these texts and performances side-by-side, within the same history, I aim to show the interdependency of these two usually separated strands of early modern studies and make a case for greater integration of the two in both editorial, historiographical, and performance practices.College of Humanities International Studentshi