Institution
University of Nebraska Omaha
Education•Omaha, Nebraska, United States•
About: University of Nebraska Omaha is a education organization based out in Omaha, Nebraska, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 4526 authors who have published 8905 publications receiving 213914 citations. The organization is also known as: UNO & University of Omaha.
Papers published on a yearly basis
Papers
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TL;DR: An overview of the different schools of thought and approaches to mitigating (social) biases and increase fairness in the Machine Learning literature is provided, organises approaches into the widely accepted framework of pre-processing, in- processing, and post-processing methods, subcategorizing into a further 11 method areas.
Abstract: As Machine Learning technologies become increasingly used in contexts that affect citizens, companies as well as researchers need to be confident that their application of these methods will not have unexpected social implications, such as bias towards gender, ethnicity, and/or people with disabilities. There is significant literature on approaches to mitigate bias and promote fairness, yet the area is complex and hard to penetrate for newcomers to the domain. This article seeks to provide an overview of the different schools of thought and approaches to mitigating (social) biases and increase fairness in the Machine Learning literature. It organises approaches into the widely accepted framework of pre-processing, in-processing, and post-processing methods, subcategorizing into a further 11 method areas. Although much of the literature emphasizes binary classification, a discussion of fairness in regression, recommender systems, unsupervised learning, and natural language processing is also provided along with a selection of currently available open source libraries. The article concludes by summarising open challenges articulated as four dilemmas for fairness research.
240 citations
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TL;DR: In relational victimization, the perpetrator attempts to harm the target through the manipulation of re-lationships, threat of damage to them, or both as discussed by the authors, and evidence that relational victimisation is harmful.
Abstract: Although many past studies of peer maltreatment have fo- cused on physical victimiza- tion, the importance of an empirical focus on relational victimization has only recently been recognized. In relational victimization, the perpetrator attempts to harm the target through the manipulation of re- lationships, threat of damage to them, or both. We review what is currently known about rela- tional victimization with three issues in mind: (a) developmen- tal changes in the manifesta- tion of relational victimization, (b) gender differences in the likelihood of being victimized, and (c) evidence that relational victimization is harmful.
238 citations
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TL;DR: In this paper, an extensive analysis of in-sample and out-of-sample tests of stock return predictability was conducted in an effort to better understand the nature of the empirical evidence on returns predictability.
238 citations
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TL;DR: Grasses New, used and rare books and ephemera at Biblio Evolutionary History of the Grasses | Plant Physiology Poaceae Wikipedia Genera Graminum.
Abstract: Grasses New, used and rare books and ephemera at Biblio Evolutionary History of the Grasses | Plant Physiology Poaceae Wikipedia Genera Graminum. Grasses of the World | SpringerLink Classification | GrassWorld Genera Graminum: Grasses of the world (Kew Bulletin ... List of Poaceae genera Wikipedia Genera graminum. Grasses of the World. Flora of Zimbabwe: Family page: Poaceae Genera Graminum: Grasses of the world, Clayton, Renvoize Poaceae Barnhart | Plants of the World Online | Kew Science The bamboos of Nepal and Bhutan. Part III: Drepanostachyum ... 9781900347754: Genera Graminum: Grasses of the world (Kew ... Genera graminum : grasses of the world / W.D. Clayton & S ... Ethnobotanical Usages of Grasses in Central Punjab-Pakistan Genera Graminum Grasses Of The Genera graminum : grasses of the world (Book, 1986 ... [PDF] Phytolith analysis of Poa pratensis (Poaceae) leaves ...
236 citations
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TL;DR: The cognitive network model (CNM) is presented as a causal model of the cognitive mechanisms that give rise to creative solutions in the human mind to explain why creativity prescriptions work as they do and provide a basis for deriving new techniques to further enhance creativity.
Abstract: Organizations must be creative continuously to survive and thrive in today's highly competitive, rapidly changing environment. A century of creativity research has produced several descriptive models creativity, and hundreds of prescriptions for interventions that demonstrably improve creativity. This paper presents the cognitive network model (CNM) as a causal model of the cognitive mechanisms that give rise to creative solutions in the human mind. The model may explain why creativity prescriptions work as they do. The model may also provide a basis for deriving new techniques to further enhance creativity. The paper tests the model in an experiment where 61 four-person groups used either free-brainstorming or one of three variations on directed-brainstorming to generate solutions for one of two unstructured tasks. In both tasks, people using directed-brainstorming produced more solutions with high creativity ratings, produced solutions with higher average creativity ratings, and produced higher concentrations of creative solutions than did people using free-brainstorming. Significant differences in creativity were also found among the three variations on directed-brainstorming. The findings were consistent with the CNM.
236 citations
Authors
Showing all 4588 results
Name | H-index | Papers | Citations |
---|---|---|---|
Darell D. Bigner | 130 | 819 | 90558 |
Dan L. Longo | 125 | 697 | 56085 |
William B. Dobyns | 105 | 430 | 38956 |
Eamonn Martin Quigley | 103 | 685 | 39585 |
Howard E. Gendelman | 101 | 567 | 39460 |
Alexander V. Kabanov | 99 | 447 | 34519 |
Douglas T. Fearon | 94 | 278 | 35140 |
Dapeng Yu | 94 | 745 | 33613 |
John E. Wagner | 94 | 488 | 35586 |
Zbigniew K. Wszolek | 93 | 576 | 39943 |
Surinder K. Batra | 87 | 564 | 30653 |
Frank L. Graham | 85 | 255 | 39619 |
Jing Zhou | 84 | 533 | 37101 |
Manish Sharma | 82 | 1407 | 33361 |
Peter F. Wright | 77 | 252 | 21498 |