LEARNFCA: A FUZZY FCA AND PROBABILITY BASED APPROACH FOR LEARNING AND CLASSIFICATION

Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering. This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of it’s applications and various approaches adopted by researchers in the areas of dataanalysis, knowledge management with emphasis to data-learning and classification problems. We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabelled features. We evaluate LearnFCA on encodings from three datasets - mnist, omniglot and cancer images with interesting results and varying degrees of success. Adviser: Jitender Deogu

Hardware Parallelization of Cores Accessing Memory with Irregular Access Patterns

This project studies FPGA-based heterogeneous computing architectures with the objective of discovering their ability to optimize the performances of algorithms characterized by irregular memory access patterns. The example used to achieve this is a graph algorithm known as Triad Census Algorithm, whose implementation has been developed and tested. First of all, the triad census algorithm is presented, explaining the possible variants and reviewing the existing implementations upon different architectures. The analysis focuses on the parallelization techniques which have allowed to boost performance, thus reducing execution time. Besides, the study tackles the OpenCL programming model, the standard used to develop the final application. Special attention is paid to the language details that have motivated some of the most important design decisions. The dissertation continues with the description of the project implementation, including the application objectives, the system design, and the different variants developed to enhance algorithm performance. Finally, some of the experimental results are presented and discussed. All implemented versions are evaluated and compared to decide which is the best in terms of scalability and execution time

Semantic networks

AbstractA semantic network is a graph of the structure of meaning. This article introduces semantic network systems and their importance in Artificial Intelligence, followed by I. the early background; II. a summary of the basic ideas and issues including link types, frame systems, case relations, link valence, abstraction, inheritance hierarchies and logic extensions; and III. a survey of ‘world-structuring’ systems including ontologies, causal link models, continuous models, relevance, formal dictionaries, semantic primitives and intersecting inference hierarchies. Speed and practical implementation are briefly discussed. The conclusion argues for a synthesis of relational graph theory, graph-grammar theory and order theory based on semantic primitives and multiple intersecting inference hierarchies

LearnFCA: A Fuzzy FCA and Probability Based Approach for Learning and Classification

Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering. This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of it’s applications and various approaches adopted by researchers in the areas of dataanalysis, knowledge management with emphasis to data-learning and classification problems. We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabelled features. We evaluate LearnFCA on encodings from three datasets - mnist, omniglot and cancer images with interesting results and varying degrees of success. Adviser: Dr Jitender Deogu

A survey of statistical network models

Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference

Exponential-Family Random Graph Models for Valued Networks

Exponential-family random graph models (ERGMs) provide a principled and flexible way to model and simulate features common in social networks, such as propensities for homophily, mutuality, and friend-of-a-friend triad closure, through choice of model terms (sufficient statistics). However, those ERGMs modeling the more complex features have, to date, been limited to binary data: presence or absence of ties. Thus, analysis of valued networks, such as those where counts, measurements, or ranks are observed, has necessitated dichotomizing them, losing information and introducing biases. In this work, we generalize ERGMs to valued networks. Focusing on modeling counts, we formulate an ERGM for networks whose ties are counts and discuss issues that arise when moving beyond the binary case. We introduce model terms that generalize and model common social network features for such data and apply these methods to a network dataset whose values are counts of interactions.Comment: 42 pages, including 2 appendixes (3 pages total), 5 figures, 2 tables, 1 algorithm listing; a substantial revision and reorganization: major changes include focus shifted to counts in particular, sections added on modeling actor heterogeneity, a subsection on degeneracy, another example, and an appendix on non-steepness of the CMP distributio

Friends or foes? Relational dissonance and adolescent psychological wellbeing

The interaction of positive and negative relationships (i.e. I like you, but you dislike me - referred to as relational dissonance) is an underexplored phenomenon. Further, it is often only poor (or negative) mental health that is examined in relation to social networks, with little regard for positive psychological wellbeing. Finally, these issues are compounded by methodological constraints. This study explores a new concept of relational dissonance alongside mutual antipathies and friendships and their association with mental health using multivariate exponential random graph models with an Australian sample of secondary school students. Results show male students with relationally dissonant ties have lower positive mental health measures. Girls with relationally dissonant ties have lower depressed mood, but those girls being targeted by negative ties are more likely to have depressed mood. These findings have implications for the development of interventions focused on promoting adolescent wellbeing and consideration of the appropriate measurement of wellbeing and mental illness

How “Struggling” Readers Engage in Literacy Events in Middle School Science: An Analysis of Interactions in Literacy Events

This study examined opportunities for participation and learning for struggling readers in a sixth grade science classroom. Literacy practices, language differences, activity structures, and the social and cultural identities and associated practices and everyday funds of knowledge of both struggling and nonstruggling readers in one sixth grade science classroom were documented and analyzed using a qualitative research design. Over sixteen hours of audio and video recordings as well as numerous student work samples were transcribed and analyzed. Analyses of the classroom interactions and artifacts documented in this study revealed several important affordances available in the context of this classroom related to opportunities for speaking and listening, some uses of print texts, and student agency in interactions. Student learning was found to be constrained by macrocontextual factors, text difficulty, and student history

Limited evidence for structural balance in the family

Published online: 17 August 2023Previous studies have shown that relationship sentiments in families follow a pattern wherein either all maintain positive relationships or there are two antagonistic factions. This result is consistent with the network theory of structural balance that individuals befriend their friends’ friend and become enemies with their friends’ enemies. Fault lines in families would then endogenously emerge through the same kinds of interactional processes that organize nations into axis and allies. We argue that observed patterns may instead exogenously come about as the result of personal characteristics or homophilous partitions of family members. Disentangling these alternate theoretical possibilities requires longitudinal data. The present study tracks the sentiment dynamics of 1,710 families in a longitudinal panel study. Results show the same static patterns suggestive of balancing processes identified in earlier research, yet dynamic analysis reveals that conflict in families is not generated or resolved in accordance with balance theory

Controlling and Organizing the Network Structure of Korean Business Groups, 1997-2003

This thesis examines organizing and controlling mechanisms within the network structure of Korean business groups, chaebols, for the family-based corporate ownership and control under environmental uncertainty. Research focuses on the groups' changing patterns of inter-firm network structures, the maneuvering strategy by utilizing relational configurations of business groups for the family members' robust control, and the effect of network structure on the corporate performance of affiliated firms. Considering the financial crisis of 1997 in South Korea and the aftermath of this crisis as a natural experiment, social network analysis is used for analyzing each of the 178 cases for 28 chaebols during 1997 to 2003. Although retaining a centralized, hierarchical form of group structure with the tau statistic, the overall inter-firm configurations of each business group, as result of concrete but simplified images of network configurations by blockmodel analysis and the comparison of them with idealized models by simple matching analysis, show the existence of variations within a monolithic form in synchronic comparison and the changing trend to be a less centralized, hierarchical form along with stable transitive patterns in diachronic comparison. Family-based corporate control, by strategically intertwining affiliated people as vicarious agents to carry out the interests of family members and sending these combinatorial equity ties to a few major firms occupying core positions, is guaranteed without losing its substantial controlling power. It is argued that, borrowing from Bourdieu's "condescension strategy," this strategically contrived control is a proactive and reactive strategy in response to environmental pressure even though this strategy is effective in certain intercorporate conditions. The estimated influence of inter-firm network structure on the corporate performance of affiliated firms is minimal in multilevel analysis. In contrast, affiliated firms having direct connections with family members show relatively better corporate performance than those that do not have these connections. The implication of this result is that the network structure of chaebols tend to be shaped, maintained, and reorganized for family-based, effective, overarching corporate control at the business group level rather than for efficient corporate performance of affiliated firms at the firm level. Finally, this thesis suggests that corporate control and corporate gain do not always go hand in hand, and economic practices need to be understood by the simultaneous consideration of pecuniary and not necessarily pecuniary but still related interests, such as control and social relations where economic practices are anchored in