Multi modal multi-semantic image retrieval

PhDThe rapid growth in the volume of visual information, e.g. image, and video can overwhelm users’ ability to find and access the specific visual information of interest to them. In recent years, ontology knowledge-based (KB) image information retrieval techniques have been adopted into in order to attempt to extract knowledge from these images, enhancing the retrieval performance. A KB framework is presented to promote semi-automatic annotation and semantic image retrieval using multimodal cues (visual features and text captions). In addition, a hierarchical structure for the KB allows metadata to be shared that supports multi-semantics (polysemy) for concepts. The framework builds up an effective knowledge base pertaining to a domain specific image collection, e.g. sports, and is able to disambiguate and assign high level semantics to ‘unannotated’ images. Local feature analysis of visual content, namely using Scale Invariant Feature Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’ model (BVW) as an effective method to represent visual content information and to enhance its classification and retrieval. Local features are more useful than global features, e.g. colour, shape or texture, as they are invariant to image scale, orientation and camera angle. An innovative approach is proposed for the representation, annotation and retrieval of visual content using a hybrid technique based upon the use of an unstructured visual word and upon a (structured) hierarchical ontology KB model. The structural model facilitates the disambiguation of unstructured visual words and a more effective classification of visual content, compared to a vector space model, through exploiting local conceptual structures and their relationships. The key contributions of this framework in using local features for image representation include: first, a method to generate visual words using the semantic local adaptive clustering (SLAC) algorithm which takes term weight and spatial locations of keypoints into account. Consequently, the semantic information is preserved. Second a technique is used to detect the domain specific ‘non-informative visual words’ which are ineffective at representing the content of visual data and degrade its categorisation ability. Third, a method to combine an ontology model with xi a visual word model to resolve synonym (visual heterogeneity) and polysemy problems, is proposed. The experimental results show that this approach can discover semantically meaningful visual content descriptions and recognise specific events, e.g., sports events, depicted in images efficiently. Since discovering the semantics of an image is an extremely challenging problem, one promising approach to enhance visual content interpretation is to use any associated textual information that accompanies an image, as a cue to predict the meaning of an image, by transforming this textual information into a structured annotation for an image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct types of information representation and modality, there are some strong, invariant, implicit, connections between images and any accompanying text information. Semantic analysis of image captions can be used by image retrieval systems to retrieve selected images more precisely. To do this, a Natural Language Processing (NLP) is exploited firstly in order to extract concepts from image captions. Next, an ontology-based knowledge model is deployed in order to resolve natural language ambiguities. To deal with the accompanying text information, two methods to extract knowledge from textual information have been proposed. First, metadata can be extracted automatically from text captions and restructured with respect to a semantic model. Second, the use of LSI in relation to a domain-specific ontology-based knowledge model enables the combined framework to tolerate ambiguities and variations (incompleteness) of metadata. The use of the ontology-based knowledge model allows the system to find indirectly relevant concepts in image captions and thus leverage these to represent the semantics of images at a higher level. Experimental results show that the proposed framework significantly enhances image retrieval and leads to narrowing of the semantic gap between lower level machinederived and higher level human-understandable conceptualisation

Learning to Love Globalization? Education and Individual Attitudes Toward International Trade

Recent studies of public attitudes toward trade have converged upon one central finding: support for trade restrictions is highest among respondents with the lowest levels of education. This has been interpreted as strong support for the Stolper-Samuelson theorem, the classic economic treatment of the income effects of trade which predicts that trade openness benefits those owning factors of production with which their economy is relatively well endowed (those with skills in the advanced economies) while hurting others (low skilled workers). We re- examine the available survey data, showing that the impact of education on attitudes toward trade is almost identical among respondents in the active labor force and those who are not (even those who are retired). We also find that, while individuals with college-level educations are far more likely to favor trade openness than others, other types of education have no significant effects on attitudes, and some actually reduce the support for trade, even though they clearly contribute to skill acquisition. Combined, these results strongly suggest that the effects of education on individual trade preferences are not primarily a product of distributional concerns linked to job skills. We suggest that exposure to economic ideas and information among college-educated individuals plays a key role in shaping attitudes toward trade and globalization. This is not to say that distributional issues are not important in shaping attitudes toward trade – just that they are not clearly manifest in the simple, broad association between education levels and support for free trade.International Trade, Trade Preferences, Stolper-Samuelson, Education Effects

Som-Based Class Discovery Exploring the ICA-Reduced Features of Microarray Expression Profiles

Gene expression datasets are large and complex, having many variables and unknown internal structure. We apply independent component analysis (ICA) to derive a less redundant representation of the expression data. The decomposition produces components with minimal statistical dependence and reveals biologically relevant information. Consequently, to the transformed data, we apply cluster analysis (an important and popular analysis tool for obtaining an initial understanding of the data, usually employed for class discovery). The proposed self-organizing map (SOM)-based clustering algorithm automatically determines the number of ‘natural’ subgroups of the data, being aided at this task by the available prior knowledge of the functional categories of genes. An entropy criterion allows each gene to be assigned to multiple classes, which is closer to the biological representation. These features, however, are not achieved at the cost of the simplicity of the algorithm, since the map grows on a simple grid structure and the learning algorithm remains equal to Kohonen’s one

Bargaining in the Shadow of the Law: Divorce Laws and Family Distress

Over the past thirty years changes in divorce law have significantly increased access to divorce. The different timing of divorce law reform across states provides a useful quasi-experiment with which to examine the effects of this change. We analyze state panel data to estimate changes in suicide, domestic violence, and spousal murder rates arising from the change in divorce law. Suicide rates are used as a quantifiable measure of wellbeing, albeit one that focuses on the extreme lower tail of the distribution. We find a large, statistically significant, and econometrically robust decline in the number of women committing suicide following the introduction of unilateral divorce. No significant effect is found for men. Domestic violence is analyzed using data on both family conflict resolution and intimate homicide rates. The results indicate a large decline in domestic violence for both men and women in states that adopted unilateral divorce. We find suggestive evidence that unilateral divorce led to a decline in females murdered by their partners, while the data revealed no discernible effects for men murdered. In sum, we find strong evidence that legal institutions have profound real effects on outcomes within families.

IDENTIFICATION OF STUDENTS AT RISK OF LOW PERFORMANCE BY COMBINING RULE-BASED MODELS, ENHANCED MACHINE LEARNING, AND KNOWLEDGE GRAPH TECHNIQUES

Technologies and online learning platforms have changed the contemporary educational paradigm, giving institutions more alternatives in a complex and competitive environment. Online learning platforms, learning-based analytics, and data mining tools are increasingly complementing and replacing traditional education techniques. However, academic underachievement, graduation delays, and student dropouts remain common problems in educational institutions. One potential method of preventing these issues is by predicting student performance through the use of institution data and advanced technologies. However, to date, scholars have yet to develop a module that can accurately predict students’ academic achievement and commitment. This dissertation attempts to bridge that gap by presenting a framework that allows instructors to achieve four goals: (1) track and monitor the performance of each student on their course, (2) identify at-risk students during the earliest stages of the course progression (3), enhance the accuracy with which at-risk student performance is predicted, and (4) improve the accuracy of student ranking and development of personalized learning interventions. These goals are achieved via four objectives. Objective One proposes a rule-based strategy and risk factor flag to warn instructors about at-risk students. Objective Two classifies at-risk students using an explainable ML-based model and rule-based approach. It also offers remedial strategies for at-risk students at each checkpoint to address their weaknesses. Objective Three uses ML-based models, GCNs, and knowledge graphs to enhance the prediction results. Objective Four predicts students’ ranking using ML-based models and clustering-based KGEs with the aim of developing personalized learning interventions. It is anticipated that the solution presented in this dissertation will help educational institutions identify and analyze at-risk students on a course-by-course basis and, thereby, minimize course failure rates

Proceedings, MSVSCC 2016

Proceedings of the 10th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 14, 2016 at VMASC in Suffolk, Virginia

Health inequalities in the older population: the role of personal capital, social resources and socio-economic circumstances.

Older people now constitute the majority of those with health problems in developed countries so an understanding of health variations in later life is increasingly important. In this paper, we use data from three rounds of the Health Survey for England, a large nationally representative sample, to analyse variations in the health of adults aged 65-84 by indicators of attributes acquired in childhood and young adulthood, termed personal capital; and by current social resources and current socio-economic circumstances, while controlling for smoking behaviour and age. We used six indicators of health status in the analysis, four based on self-reports and two based on nurse collected data, which we hypothesised would identify different dimensions of health. Results showed that socio-economic indicators, particularly receipt of income support (a marker of poverty) were most consistently associated with raised odds of poor health outcomes. Associations between marital status and health were in some cases not in the expected direction. This may reflect bias arising from exclusion of the institutional population (although among those under 85 the proportion in institutions is very low) but merits further investigation, especially as the marital status composition of the older population is changing. Analysis of deviance showed that social resources (marital status and social support) had the greatest effect on the indicator of psychological health (GHQ) and also contributed significantly to variation in self-rated health, but among women not to variation in taking three or more medicines and among men not to self-reported long-standing illnesses. Smoking, in contrast, was much more strongly associated with these indicators than with self-rated health. These results are consistent with the view that self-rated health may provide a holistic indicator of health in the sense of well-being, whereas measures such as taking prescribed medications may be more indicative of specific morbidities. The results emphasise again the need to consider both socio-economic and socio-psychological influences on later life health

Theoretical and Practical Advances in Computer-based Educational Measurement

This open access book presents a large number of innovations in the world of operational testing. It brings together different but related areas and provides insight in their possibilities, their advantages and drawbacks. The book not only addresses improvements in the quality of educational measurement, innovations in (inter)national large scale assessments, but also several advances in psychometrics and improvements in computerized adaptive testing, and it also offers examples on the impact of new technology in assessment. Due to its nature, the book will appeal to a broad audience within the educational measurement community. It contributes to both theoretical knowledge and also pays attention to practical implementation of innovations in testing technology

Phase Transitions of Civil Unrest across Countries and Time

Phase transitions, characterized by abrupt shifts between macroscopic patterns of organization, are ubiquitous in complex systems. Despite considerable research in the physical and natural sciences, the empirical study of this phenomenon in societal systems is relatively underdeveloped. The goal of this study is to explore whether the dynamics of collective civil unrest can be plausibly characterized as a sequence of recurrent phase shifts, with each phase having measurable and identifiable latent characteristics. Building on previous efforts to characterize civil unrest as a self-organized critical system, we introduce a macro-level statistical model of civil unrest and evaluate its plausibility using a comprehensive dataset of civil unrest events in 170 countries from 1946 to 2017. Our findings demonstrate that the macro-level phase model effectively captures the characteristics of civil unrest data from diverse countries globally and that universal mechanisms may underlie certain aspects of the dynamics of civil unrest. We also introduce a scale to quantify a country's long-term unrest per unit of time and show that civil unrest events tend to cluster geographically, with the magnitude of civil unrest concentrated in specific regions. Our approach has the potential to identify and measure phase transitions in various collective human phenomena beyond civil unrest, contributing to a better understanding of complex social systems.Comment: Main paper (57 pages); Supporting Information (144 pages) will be available upon request. To appear in npj Complexit