1,720,960 research outputs found
Data science approaches to data center sustainability and transportation predictive analytics
Full text linkToday's plethora of available data has brought about technological advancement in multiple facets of life. One area that has burgeoned is the development of intelligent cities that leverage technology to deliver improved quality of life. The concept of smart cities involves the incorporation of digital intelligence to urban systems with the hope that improvements in specific functions of a city can, all together, yield benefits in terms of resident satisfaction and the broader societal goals of efficiency and sustainability. In this dissertation, we study the advancement of smart cities through the development of the sub-areas of smart energy and transportation.
Regarding smart energy, we focus on data center sustainability and data center inclusion in the smart grid to extract flexibility and ease congestion in various scenarios. We leverage queuing system theory along with optimization techniques to model data centers and propose demand response models in various settings where we highlight the benefits of data center inclusion in the smart grid. We first propose frameworks for data center interaction with an aggregator that attempts to extract flexibility from the data centers via a price incentive in a network-less setting. We further consider this setting for the case where data centers have the ability to service different types of jobs. Finally, we consider the network setting where data centers are included in the transmission grid with other loads and generators and highlight their ability to provide benefits to the grid.
Regarding smart transportation, we focus on predictive analytics for transportation using machine learning and deep learning approaches. Specifically, we propose state-of-the-art transformer-based architectures for the task of destination prediction using artificial and real human movement data in both indoor and outdoor settings. Destination prediction involves the use of movement data to create intelligent models that can predict intended destinations given partial trajectory information. The use of such a system that knows in advance where a user intends to travel to can offer many functionalities. In an outdoor setting, tailored advertisements, adjacent points of interest, and open parking spots to a destination can be broadcasted to a user. In an indoor setting, a destination prediction system can allow for smart elevator control in large multi-floor buildings and the creation of density-based security systems that focus on high interest areas in a building.
The contributions of this dissertation serve as strong building blocks for the development of smart cities and lead to multiple exciting directions for future work.2025-01-16T00:00:00
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Diverse Gaussian Noise Consistency Regularization for Robustness and Uncertainty Calibration
Full text linkDeep neural networks achieve high prediction accuracy when the train and test
distributions coincide. In practice though, various types of corruptions occur
which deviate from this setup and cause severe performance degradations. Few
methods have been proposed to address generalization in the presence of
unforeseen domain shifts. In particular, digital noise corruptions arise
commonly in practice during the image acquisition stage and present a
significant challenge for current methods. In this paper, we propose a diverse
Gaussian noise consistency regularization method for improving robustness of
image classifiers under a variety of corruptions while still maintaining high
clean accuracy. We derive bounds to motivate and understand the behavior of our
Gaussian noise consistency regularization using a local loss landscape
analysis. Our approach improves robustness against unforeseen noise corruptions
by 4.2-18.4% over adversarial training and other strong diverse data
augmentation baselines across several benchmarks. Furthermore, it improves
robustness and uncertainty calibration by 3.7% and 5.5%, respectively, against
all common corruptions (weather, digital, blur, noise) when combined with
state-of-the-art diverse data augmentations.Comment: Accepted to IJCNN 2023. Preliminary version accepted to ICML 2021
Uncertainty & Robustness in Deep Learning Worksho
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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