1,721,012 research outputs found
Low-Resource Neural Adaptation: A Unified Data Adaptation Framework for Neural Networks
No full textThesis (Ph.D.)--University of Washington, 2022Many machine learning (ML) models are trained on specific datasets for specific tasks. While traditional transfer learning can adapt to new datasets when labeled data are adequate, adapting to small datasets is still a challenging task. Researchers have applied multi-task learning, meta-learning, weakly-supervised learning, self-supervision, generative adversarial training, and active learning for various data adaptation applications. However, a unified data adaptation framework has yet to be developed. This study proposes a unified framework that can adapt to small datasets in a dynamic environment. Our framework, with a versatile encoder and various decoders, can simultaneously learn from source datasets and estimate confidence for novel data samples. We apply the framework to real-world medical imaging, affective computing, eye-tracking analysis, and database management applications
Low-Resource Neural Adaptation: A Unified Data Adaptation Framework for Neural Networks
No full textThesis (Ph.D.)--University of Washington, 2022Many machine learning (ML) models are trained on specific datasets for specific tasks. While traditional transfer learning can adapt to new datasets when labeled data are adequate, adapting to small datasets is still a challenging task. Researchers have applied multi-task learning, meta-learning, weakly-supervised learning, self-supervision, generative adversarial training, and active learning for various data adaptation applications. However, a unified data adaptation framework has yet to be developed. This study proposes a unified framework that can adapt to small datasets in a dynamic environment. Our framework, with a versatile encoder and various decoders, can simultaneously learn from source datasets and estimate confidence for novel data samples. We apply the framework to real-world medical imaging, affective computing, eye-tracking analysis, and database management applications
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Gaze is more than just a point: Rethinking visual attention analysis using peripheral vision-based gaze mapping
No full textVisual Expertise in Code Reviews
No full textThis study uses holistic models of image perception to analyze and interpret eye movements during a code review. 23 participants (15 novices and 8 experts) take part in the experiment. The subjects’ task is to review six short code examples in C programming language and identify possible errors. During the experiment, their eye movements are recorded by an SMI 250 REDmobile. Additional data is collected through questionnaires and retrospective interviews. The results implicate that holistic models of image perception provide a suitable theoretical background for the analysis and interpretation of eye movements during code reviews. The assumptions of these models are particularly evident for expert programmers. Their approach can be divided into different phases with characteristic eye movement patterns. It is best described as switching between scans of the code example (global viewing) and the detailed examination of errors (focal viewing)
Gaze-based Interaction on Handheld Mobile Devices
Get PDFWith the advancement of smartphone technology, it is now possible for smartphones to run eye-tracking using the front-facing camera, enabling hands-free interaction by empowering mobile users with novel gaze-based input techniques. While several gaze-based interaction techniques have been proposed in the literature, these techniques were deployed in settings different from daily gaze interaction with mobile devices, posing several unique challenges. The user’s holding posture may hinder the camera’s view of their face during the interaction, the front-facing camera may be obstructed by the user’s clothing or hands, or the environment is shaky due to the user’s movements and the dynamic environment. This PhD research investigates the usability of state-of-the-art gaze-based input techniques in mobile settings, develops a novel concept of combining multiple gaze-based techniques, and addresses the challenges imposed by the unique aspects of these devices
Sonography data science
Get PDFFetal sonography remains a highly specialised skill in spite of its necessity and importance. Because of differences in fetal and maternal anatomy, and human pyschomotor skills, there is an intra- and inter-sonographer variability amoungst expert sonographers. By understanding their similarities and differences, we want to build more interpretive models to assist a sonographer who is less experienced in scanning.
This thesis’s contributions to the field of fetal sonography can be grouped into two themes. First I have used data visualisation and machine learning methods to show that a sonographer’s search strategy is anatomical (plane) dependent. Second, I show that a sonographer’s style and human skill of scanning is not easily disentangled.
We first examine task-specific spatio-temporal gaze behaviour through the use of data visualisation, where a task is defined as a specific anatomical plane the sonographer is searching for. The qualitative analysis is performed at both a population and individual level, where we show that the task being performed
determines the sonographer’s gaze behaviour.
In our population-level analysis, we use unsupervised methods to identify meaningful gaze patterns and visualise task-level differences. In our individual-level analysis, we use a deep learning model to provide context to the eye-tracking data with respect to the ultrasound image. We then use an event-based visualisation to understand differences between gaze patterns of sonographers performing the same task.
In some instances, sonographers adopt a different search strategy which is seen in the misclassified instances of an eye-tracking task classification model. Our task classification model supports the qualitative behaviour seen in our population-level analysis, where task-specific gaze behaviour is quantitatively distinct.
We also investigate the use of time-based skill definitions and their appropriateness in fetal ultrasound sonography; a time-based skill definition uses years of clinical experience as an indicator of skill. The developed task-agnostic skill classification model differentiates gaze behaviour between sonographers in training and fully qualified sonographers. The preliminary results also show that fetal sonography scanning remains an operator-dependent skill, where the notion of human skill and individual scanning stylistic differences cannot be easily disentangled.
Our work demonstrates how and where sonographers look at whilst scanning, which can be used as a stepping stone for building style-agnostic skill models
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