1,720,996 research outputs found
View-Based Object Recognition Using Saliency Maps
No full textWe introduce a novel view-based object representation, called the saliency map graph (SMG), which captures the salient regions of an object view at multiple scales using a wavelet transform. This compact representation is highly invariant to translation, rotation (image and depth), and scaling, and offers the locality of representation required for occluded object recognition. To compare two saliency map graphs, we introduce two graph similarity algorithms. The first computes the topological similarity between two SMG's, providing a coarse-level matching of two graphs. The second computes the geometrical similarity between two SMG's, providing a fine-level matching of two graphs. We test and compare these two algorithms on a large database of model object viewsTechnical report DCS-TR-33
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
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
Exploring the application of process mining: process model discovery, data generation, and event prediction
No full textProcess mining is a prominent research field that has emerged in recent years. It offers substantial benefits in decision-making, efficiency enhancement, and error reduction across various real-world domains such as healthcare, business, and education. Process mining field mainly analyzes process data, typically stored in event logs, which capture case IDs, activities, and other chronological context. Processes exhibit variability based on different use cases or contexts. Researchers apply process mining techniques to extract non-trivial knowledge from the process data. However, a prevalent issue in this field is in the limited publicly available datasets that lack the complexity and diversity of real-world scenarios, which limited the development of generalized process mining methods and their practical applicability to complex problems. This dissertation aims to address this gap by exploring process mining applications across datasets of varying complexity. We proposed novel methods to enhance the representation of complex process data, mitigate challenges associated with limited data, and support human decision-making. Specifically, the dissertation presents three key contributions: (1) a method for discovering interpretable process model; (2) a deep generative model for augmenting process dataset; and (3) deep learning-based models for event prediction based on process data. The effectiveness of these methods is demonstrated through experiments conducted in real-world medical settings, such as trauma resuscitation, where they substantially improved medical providers' decision-making processes. Although the focus of this dissertation is on medical processes, the approaches developed are applicable to similar domains that using process datasets. First, we introduced a process model discovery method for complex medical processes. To develop a medical process model that improves patient outcomes, understanding the actual work (i.e., “work-as-done”) rather than theorized work (i.e., “work-as-imagined”) is critical. When applied to complex process data, traditional process model discovery methods often omits critical steps or produces cluttered and unreadable models. To address these limitations, we introduced a Trace Alignment-based Process Discovery method called TAD Miner to build interpretable process models. TAD Miner creates simple linear process models using a threshold metric that optimizes the consensus sequence to represent the backbone process, and identifies both concurrent activities and uncommon-but-critical activities to represent the side branches. TAD Miner also identifies the locations of repeated activities, which is an essential feature for representing medical treatment steps. We used the TAD process models to identify (1) the errors and (2) the best locations for the tentative steps in knowledge-driven expert models. The knowledge-driven models were revised based on the modifications suggested by the discovered models. The improved modeling using TAD Miner may enhance understanding of complex medical processes. Second, we introduced a data augmentation model for synthetic process data generation. Because process data with confidential information cannot be shared, research is limited using process data and analytics in the process mining domain. We introduced a generative adversarial network, called ProcessGAN, to address the limitation of shareable process data. Our model generates process data that consists of activity sequences and corresponding timestamps. ProcessGAN consists of a transformer-based network as the generator, and a time-aware self-attention network as the discriminator. It can generate privacy-preserving process data from random noise. ProcessGAN considers the duration of the process and time intervals between activities to generate realistic activity sequences with timestamps. To evaluate the synthetic data, we proposed statistical metrics and trained a supervised model to score the synthetic processes. We also applied our TAD Miner to discover process models (i.e., workflow diagrams) for synthetic medical processes and had domain experts evaluate the clinical applicability of the synthetic diagrams. ProcessGAN outperformed the existing generative models in generating complex processes with valid parallel pathways, and better represented the long-range dependencies between activities. The timestamps generated by the ProcessGAN model showed similar distributions and trends with the authentic timestamps. ProcessGAN can generate shareable synthetic process data indistinguishable from authentic data. Last, we introduced deep learning-based event prediction models specifically to assist the decision-making in the medical process. We introduced (1) a treatment activity recommendation model, and (2) a goal recognition model. Both models have the potential to improve workflow, reduce team errors, and improve patient outcomes. The treatment activity recommendation model aggregates patient context (e.g., injury features and physiological values) and process context (e.g., already performed activities) as input, and continuously predicts the activities to be performed. Our model is built on a tower-structured model of multi-layer perceptrons. It achieved a 0.89 F1-score on the sepsis activity predictions and 0.65 F1-score on the trauma resuscitation activity predictions, showing the feasibility of predicting activities using context and process data. The goal recognition model aims to track whether certain goals are being pursued by the medical providers throughout a medical process. Our experiments focused on recognizing the providers' goal of (1) airway stabilization and (2) circulatory support, which are two critical goals in trauma resuscitation. We designed a dual-gated recurrent unit (GRU) architecture to learn the contributions from timestamp and activity types for goal recognition. Our model achieved an average AUC score of 0.84 for airway stabilization and 0.83 for circulatory support. The results are explainable by identifying the significant time windows and the most contributory activities in deciding whether the goal is being pursuit. Our model outperformed existing medical event predictive models in both accuracy and interpretability. Integrating our model into a decision support system would automate the tracking of providers’ actions, optimizing workflow to ensure timely delivery of patient care.Ph.D.Includes bibliographical reference
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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