1,720,964 research outputs found
An Investigation into Generating High-quality, Diversified Datasets of Microbiological Images for Supervised Computer Vision Models
Get PDFSupervised deep neural networks need datasets for training, in which the data need to be annotated before use. For developing a reliable deep neural network, the datasets should meet some criteria including high-quality annotation, diversity, and abundance of data. Generation of such datasets is costly and time-consuming, especially in the case of image datasets. This is due to reasons including inaccessibility to large-scale and diverse images, as well as the laborious process of image annotation. These problems are exacerbated in the medical domain since medical image collection is more expensive, and their annotation requires in-depth domain knowledge. Thus, big data and high-quality annotation are two of the most difficult challenges in annotation of medical images, not to mention ethical considerations. The computer vision community has put forward a lot of effort to tackle these challenges, e.g., by using computer techniques for synthetically generating low-cost (economically, time-wise, etc) images or using computer techniques to facilitate the annotation process. Despite intensive efforts, many aspects of the domain and solutions remain understudied. For example, in crowdsourcing, which is a common way of generating rapid and cost-effective annotation, there is the risk of having lowskilled annotators, which degrades the annotation quality. Moreover, the tedious nature of some annotation tasks can detrimentally affect annotators’ quality in the prolonged annotation processes (even for the skilled workers). Thus, in this Ph.D. thesis, some of these challenges were comprehensively explored and some solutions, focusing on three studies outlined as follows were proposed to bridge these gaps.
First, as the prerequisite of this Ph.D. thesis, a web-based annotation platform was developed for image datasets annotations, powered by a crowdsourcing tool that has been utilized for the forthcoming studies. This platform is now available online at www.aiconsole.com. Furthermore, a dataset of microbiological images of three different parasite groups were collected and annotated by the biologist research partners. In the first study, we compared the performance of an AI-based assistive tool to help annotators (also known as crowd workers or crowd annotators in crowdsourcing context) with microbiological image annotation with that of manual annotation. To accomplish this, the web-based annotation platform was integrated with a novel assistive tool (based
on a weakly trained object detection model), and a two-day experiment (i.e. with using and not using assisitive tool, respectivly) with crowd workers was conducted in two modes:
i) AI-based assistive annotation and ii) manual annotation. A set of quantitative evaluations were conducted in order to assess the annotators' behaviour and the assistive tool's performance. Overall, the results showed how this assistive tool based on a weakly trained object detection model can decrease the annotation cost (measured by time and number of clicks). Derived from the findings of this study, some recommendations on how future platforms with the same assistive tool can be designed to more engage the annotators to the task for a better performance are provided. Due to the lack of more conclusive results related to annotators' behaviour, and fatigues effect on annotators' performance, the platform was upgraded with additional tools to address other research questions in the next study.
The second study, aimed to answer three research questions. i) How crowd workers' performance changes over time when involved in a prolonged task ii) feasibility of assessing annotators’ fatigue and performance via annotation-based and mouse-based features iii) assessing a new aggregation technique to combine crowd workers annotations with respect to their annotations’ estimated quality. In this study, we found an increase and decrease in annotators’ performance (as measured by the Dice Similarity Coefficient; DSC) as a function of learning and fatigue effects whereas workers in the learing region gained experience resulting in better performance, while in the fatigue region their performance detoriated. A set of extracted annotation-related and mouse-related features demonstrated a strong correlation with the workers' quality and fatigue level, which motivated the creation of regression models for estimating workers' performance.
Additionally, we proposed a new Weighted Majority Voting (WMV) method for aggregating annotations that takes into account the estimated quality of each individual annotation. In comparison with the benchmark aggregation techniques (conventional majority voting and STAPLE), the new aggregation method showed a relative improvement in the mean and variance of DSCs. The third study, tackled the lack of diversity in microbiology image datasets by developing a GAN-based image-to-image translation model (BioGAN) for converting microbiology images, taken in the lab into images with the visual characteristics of images taken in the field. This study was motivated by the fact that collecting microbiological images in the field is not as simple and affordable as lab-based image collection. By adding a Perceptual loss (including two elements of Content reconstruction loss and Style reconstruction loss)
to the Adversarial loss of a classical GAN network, the difference between high-level (texture) features of the synthetic image and a real-world field image has been penalised.
Then, the proposed BioGAN model was tested on its ability to translate laboratory-taken images of Prototheca into field-like images, using experts’ qualitative evaluation and quantitative evaluation by the Mask R-CNN object detection framework. We found that the generated images helped to boost diversity as well as the volume of the dataset. In synthetically generated images, the spatial characteristics remain the same (i.e., the cells remain in the same position with the same dimension), which means that the
annotations for the lab-taken images are valid and usable for synthetic field images, which reduces the cost of annotation. These findings and developed models extended theoretical and practical knowledge in the area of medical image annotation, creating a low-cost but high-quality image dataset for supervised computer vision models based on neural networks. Specifically, the contribution lies in i) providing AI-based tools for computer vision practitioners and researchers to generate cost-effective yet high-quality annotations on image datasets, ii) developing a set of guidelines to help developers design better crowdsourcing platforms, iii) understanding users' behaviour and interactions in crowdsourcing environments, iv) aggregating annotations from crowdsourcing workers more effectively, v) the potential use of a GAN model for enhancing the diversity of image datasets. Also, as one of the major practical contributions of this PhD, the crowdsourcing image annotation platform, and the codes for the image translation model have been published for use by practitioners
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
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Supporting social interaction for older users in game-like 3D virtual worlds
No full textThere has been ongoing research into how 3D virtual worlds can be designed to support everyday life activities such as meeting friends, shopping, obtaining health information, etc. In this paper, we report a study with 38 older participants engaging with 3D and non-3D virtual grocery stores in order to identify key factors which affected their experience and satisfaction in social engagement within the virtual environment. A mixed method of questionnaire and contextual interview were used for data collection and analysis. We present some preliminary finding within the scope of this position paper, focusing on various aspects of virtual world such as avatars, non-verbal communication cues, etc
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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