3,820 research outputs found
Dispelling the Myths Behind First-author Citation Counts
No full textWe 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
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Online Supplement 2 Appendix for Bowers, Zhao, and Ho (2023) Towards Hierarchical Cluster Analysis Heatmaps: R Tutorial and code
No full textThis document is the Hierarchical Cluster Analysis Heatmaps in R Tutorial and code for the Online Supplement 2 Appendix for the paper:
Bowers, A.J., Zhao, Y., Ho, E. (2023) Towards Hierarchical Cluster Analysis Heatmaps as Visual Data Analysis of Entire Student Cohort Longitudinal Trajectories and Outcomes from Grade 9 through College. The High School Journal
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Online Supplement 1 Appendix for Bowers, Zhao, and Ho (2023) Towards Hierarchical Cluster Analysis Heatmaps: R Shiny application code
No full textThis document is the R Shiny application code for the Online Supplement 1 Appendix for the paper:
Bowers, A.J., Zhao, Y., Ho, E. (2023) Towards Hierarchical Cluster Analysis Heatmaps as Visual Data Analysis of Entire Student Cohort Longitudinal Trajectories and Outcomes from Grade 9 through College. The High School Journal
Automaattinen hermoverkko-oppiminen ihmisen käyttäytymisen ymmärtämiseksi
No full textAbstractUnderstanding human behavior is one of the most pivotal steps toward real-world Artificial Intelligence (AI) or even Artificial general intelligence (AGI). However, this task is challenging as human social attributes make human beings unique, leading to various and complicated behaviors. Moreover, real-life behavior data are normally high-dimensional with dynamic changes or even non-Euclidean structures, involving multiple modalities. Currently, one of the first alternatives to addressing these challenges is using deep neural networks or deep learning, which has brought revolutionary changes in data computation and computer sciences. Nevertheless, expert knowledge of both neural architecture design and human behavior analysis is expected more than ever before in this interdisciplinary research field. All these issues spur the current deep learning studies towards automatic deep neural network learning, which could automatically sketch a neural architecture for a given behavior analysis task. In line with this topic, this thesis explores the automatic neural network learning approach for human behavior understanding from the most representative behaviors, including human facial expression and actions, step by step. First, manually designed computational models are proposed for human facial expression and actions with dynamic information and graph structures. Based on this, to free humans from the exhausting process, more advanced methods, i.e., automatic neural network learning, are presented. Extensive experiments on benchmark facial expression datasets and action recognition datasets are conducted and comparison results prove the effectiveness of the proposed methods.Original papersOriginal papers are not included in the electronic version of the dissertation.Peng, W., Hong, X., Xu, Y., & Zhao, G. (2019). A boost in revealing subtle facial expressions: A consolidated eulerian framework. 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), 1–5. https://doi.org/10.1109/FG.2019.8756541Self-archived versionPeng, W., Hong, X., Zhao, G., & Cambria, E. (2021). Adaptive modality distillation for separable multimodal sentiment analysis. IEEE Intelligent Systems, 36(3), 82–89. https://doi.org/10.1109/MIS.2021.3057757Self-archived versionXia, Z., Peng, W., Khor, H.-Q., Feng, X., & Zhao, G. (2020). Revealing the invisible with model and data shrinking for composite-database micro-expression recognition. IEEE Transactions on Image Processing, 29, 8590–8605. https://doi.org/10.1109/TIP.2020.3018222Self-archived versionPeng, W., Hong, X., & Zhao, G. (2019). Video action recognition via neural architecture searching. 2019 IEEE International Conference on Image Processing (ICIP), 11–15. https://doi.org/10.1109/ICIP.2019.8802919Self-archived versionPeng, W., Shi, J., & Zhao, G. (2021). Spatial temporal graph deconvolutional network for skeleton-based human action recognition. IEEE Signal Processing Letters, 28, 244–248. https://doi.org/10.1109/LSP.2021.3049691Self-archived versionPeng, W., Hong, X., & Zhao, G. (2021). Tripool: Graph triplet pooling for 3D skeleton-based action recognition. Pattern Recognition, 115, 107921. https://doi.org/10.1016/j.patcog.2021.107921Self-archived versionPeng, W., Shi, J., Varanka, T., & Zhao, G. (2021). Rethinking the ST-GCNs for 3D skeleton-based human action recognition. Neurocomputing, 454, 45–53. https://doi.org/10.1016/j.neucom.2021.05.004Self-archived versionPeng, W., Varanka, T., Mostafa, A., Shi, H., & Zhao, G. (2021). Hyperbolic deep neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/TPAMI.2021.3136921Self-archived versionPeng, W., Hong, X., Chen, H., & Zhao, G. (2020). Learning graph convolutional network for skeleton-based human action recognition by neural searching. Proceedings of the AAAI Conference on Artificial Intelligence, 34(03), 2669–2676. https://doi.org/10.1609/aaai.v34i03.5652Self-archived versionPeng, W., Shi, J., Xia, Z., & Zhao, G. (2020). Mix dimension in poincaré geometry for 3d skeleton-based action recognition. Proceedings of the 28th ACM International Conference on Multimedia, 1432–1440. https://doi.org/10.1145/3394171.3413910Self-archived versionTiivistelmäIhmisten käyttäytymisen ymmärtäminen on yksi keskeisistä askeleista kohti todellista tekoälyä (AI) tai jopa yleistä tekoälyä (AGI). Tämä tehtävä on kuitenkin haastava, sillä ihmisen sosiaaliset ominaisuudet tekevät ihmisistä ainutlaatuisia, mikä johtaa erilaisiin ja monimutkaisiin käyttäytymismalleihin. Lisäksi tosielämän käyttäytymisdata on yleensä korkeaulotteinen, ja siinä on dynaamisia muutoksia tai jopa ei-euklidisia rakenteita, joihin liittyy useita modaliteetteja. Tällä hetkellä yksi ensimmäisistä vaihtoehdoista haasteisiin vastaamiseksi on syvän neuroverkon tai syväoppimisen käyttö, joka on tuonut mukanaan Vallankumoukselliset muutokset tietojen laskennassa ja tietojenkäsittelytieteissä. Asiantuntijatietoa sekä hermoarkkitehtuurin suunnittelusta että ihmisen käyttäytymisen analysoinnista odotetaan kuitenkin enemmän kuin koskaan aiemmin tällä tieteidenvälisellä tutkimusalueella. Kaikki nämä kysymykset kannustavat nykyisiä syväoppimistutkimuksia kohti automaattista syvän neuroverkon oppimista, joka voisi automaattisesti luonnostella hermoarkkitehtuurin tietylle käyttäytymisanalyysille Tämän aiheen mukaisesti opinnäytetyössä tutkitaan vaihe vaiheelta automaattista neuroverk- kooppimisen lähestymistapaa ihmisen käyttäytymisen ymmärtämiseen edustavimmista käyttäytymismalleista, mukaan lukien ihmisen ilmeet ja toiminnot. Ensin ehdotetaan manuaalisesti suunniteltuja laskennallisia malleja ihmisen ilmeille ja toiminnalle dynaamisilla tiedoilla ja graafirakenteilla, joiden pohjalta ihmisen uuvuttavasta edistymisestä vapauttamiseksi esitetään edistyneempiä menetelmiä, ie, automaattinen hermoverkkooppiminen. Kattavia kokeita benchmark ME-tietosarjoista ja toiminnantunnistustietosarjoista tehdään ja vertailutulokset osoittavat ehdotettujen menetelmien tehokkuuden.OsajulkaisutOsajulkaisut eivät sisälly väitöskirjan elektroniseen versioon.Peng, W., Hong, X., Xu, Y., & Zhao, G. (2019). A boost in revealing subtle facial expressions: A consolidated eulerian framework. 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), 1–5. https://doi.org/10.1109/FG.2019.8756541Rinnakkaistallennettu versioPeng, W., Hong, X., Zhao, G., & Cambria, E. (2021). Adaptive modality distillation for separable multimodal sentiment analysis. IEEE Intelligent Systems, 36(3), 82–89. https://doi.org/10.1109/MIS.2021.3057757Rinnakkaistallennettu versioXia, Z., Peng, W., Khor, H.-Q., Feng, X., & Zhao, G. (2020). Revealing the invisible with model and data shrinking for composite-database micro-expression recognition. IEEE Transactions on Image Processing, 29, 8590–8605. https://doi.org/10.1109/TIP.2020.3018222Rinnakkaistallennettu versioPeng, W., Hong, X., & Zhao, G. (2019). Video action recognition via neural architecture searching. 2019 IEEE International Conference on Image Processing (ICIP), 11–15. https://doi.org/10.1109/ICIP.2019.8802919Rinnakkaistallennettu versioPeng, W., Shi, J., & Zhao, G. (2021). Spatial temporal graph deconvolutional network for skeleton-based human action recognition. IEEE Signal Processing Letters, 28, 244–248. https://doi.org/10.1109/LSP.2021.3049691Rinnakkaistallennettu versioPeng, W., Hong, X., & Zhao, G. (2021). Tripool: Graph triplet pooling for 3D skeleton-based action recognition. Pattern Recognition, 115, 107921. https://doi.org/10.1016/j.patcog.2021.107921Rinnakkaistallennettu versioPeng, W., Shi, J., Varanka, T., & Zhao, G. (2021). Rethinking the ST-GCNs for 3D skeleton-based human action recognition. Neurocomputing, 454, 45–53. https://doi.org/10.1016/j.neucom.2021.05.004Rinnakkaistallennettu versioPeng, W., Varanka, T., Mostafa, A., Shi, H., & Zhao, G. (2021). Hyperbolic deep neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/TPAMI.2021.3136921Rinnakkaistallennettu versioPeng, W., Hong, X., Chen, H., & Zhao, G. (2020). Learning graph convolutional network for skeleton-based human action recognition by neural searching. Proceedings of the AAAI Conference on Artificial Intelligence, 34(03), 2669–2676. https://doi.org/10.1609/aaai.v34i03.5652Rinnakkaistallennettu versioPeng, W., Shi, J., Xia, Z., & Zhao, G. (2020). Mix dimension in poincaré geometry for 3d skeleton-based action recognition. Proceedings of the 28th ACM International Conference on Multimedia, 1432–1440. https://doi.org/10.1145/3394171.3413910Rinnakkaistallennettu versioAcademic dissertation to be presented with the assent of the Doctoral Training Committee of Technology and Natural Sciences of the University of Oulu for public defence in the OP auditorium (L10), Linnanmaa, on 14 April 2022, at 12 noonAbstract
Understanding human behavior is one of the most pivotal steps toward real-world Artificial Intelligence (AI) or even Artificial general intelligence (AGI). However, this task is challenging as human social attributes make human beings unique, leading to various and complicated behaviors. Moreover, real-life behavior data are normally high-dimensional with dynamic changes or even non-Euclidean structures, involving multiple modalities. Currently, one of the first alternatives to addressing these challenges is using deep neural networks or deep learning, which has brought revolutionary changes in data computation and computer sciences. Nevertheless, expert knowledge of both neural architecture design and human behavior analysis is expected more than ever before in this interdisciplinary research field. All these issues spur the current deep learning studies towards automatic deep neural network learning, which could automatically sketch a neural architecture for a given behavior analysis task. In line with this topic, this thesis explores the automatic neural network learning approach for human behavior understanding from the most representative behaviors, including human facial expression and actions, step by step. First, manually designed computational models are proposed for human facial expression and actions with dynamic information and graph structures. Based on this, to free humans from the exhausting process, more advanced methods, i.e., automatic neural network learning, are presented. Extensive experiments on benchmark facial expression datasets and action recognition datasets are conducted and comparison results prove the effectiveness of the proposed methods.Tiivistelmä
Ihmisten käyttäytymisen ymmärtäminen on yksi keskeisistä askeleista kohti todellista tekoälyä (AI) tai jopa yleistä tekoälyä (AGI). Tämä tehtävä on kuitenkin haastava, sillä ihmisen sosiaaliset ominaisuudet tekevät ihmisistä ainutlaatuisia, mikä johtaa erilaisiin ja monimutkaisiin käyttäytymismalleihin. Lisäksi tosielämän käyttäytymisdata on yleensä korkeaulotteinen, ja siinä on dynaamisia muutoksia tai jopa ei-euklidisia rakenteita, joihin liittyy useita modaliteetteja. Tällä hetkellä yksi ensimmäisistä vaihtoehdoista haasteisiin vastaamiseksi on syvän neuroverkon tai syväoppimisen käyttö, joka on tuonut mukanaan Vallankumoukselliset muutokset tietojen laskennassa ja tietojenkäsittelytieteissä. Asiantuntijatietoa sekä hermoarkkitehtuurin suunnittelusta että ihmisen käyttäytymisen analysoinnista odotetaan kuitenkin enemmän kuin koskaan aiemmin tällä tieteidenvälisellä tutkimusalueella. Kaikki nämä kysymykset kannustavat nykyisiä syväoppimistutkimuksia kohti automaattista syvän neuroverkon oppimista, joka voisi automaattisesti luonnostella hermoarkkitehtuurin tietylle käyttäytymisanalyysille Tämän aiheen mukaisesti opinnäytetyössä tutkitaan vaihe vaiheelta automaattista neuroverk- kooppimisen lähestymistapaa ihmisen käyttäytymisen ymmärtämiseen edustavimmista käyttäytymismalleista, mukaan lukien ihmisen ilmeet ja toiminnot. Ensin ehdotetaan manuaalisesti suunniteltuja laskennallisia malleja ihmisen ilmeille ja toiminnalle dynaamisilla tiedoilla ja graafirakenteilla, joiden pohjalta ihmisen uuvuttavasta edistymisestä vapauttamiseksi esitetään edistyneempiä menetelmiä, ie, automaattinen hermoverkkooppiminen. Kattavia kokeita benchmark ME-tietosarjoista ja toiminnantunnistustietosarjoista tehdään ja vertailutulokset osoittavat ehdotettujen menetelmien tehokkuuden
Gated relational stacked denoising autoencoder with localized author embedding for global citation recommendation
No full textCitation recommendation is an effective and efficient way to facilitate authors finding desired references. This paper presents a novel neural network based model, called gated relational probabilistic stacked denoising autoencoder with localized author (GRSLA) embedding, for global citation recommendation task. Our model is comprised of two modules with different neural network architecture. For each citing and cited papers, we use a gated paper embedding module, which is extended from probabilistic stacked denoising autoencoder (PSDAE) by adding gated units, to obtain their paper vectors. The added gated units are able to utilize text information of cited paper to refine the vector representation of citing paper in multiple semantic levels. For an author in papers, we first apply topic model to obtain his/her semantic neighbors, and then use a localized author embedding (LAE) module to excavate author vector representation from semantic and explicit neighbors. Unlike most graph convolutional network (GCN) based methods, the LAE module is able to avoid computing global Laplacian in whole graph by taking limited neighbors. Moreover, the LAE module can also be stacked to absorb more neighbors, which makes our model have high extendibility. Based on the generation process of GRSLA, we also derive a learning algorithm of our model by maximum a posteriori (MAP) estimation. We conduct experiments on the AAN, DBLP and CORD-19 datasets, and the results show that GRSLA model works well than previous global citation recommendation methods
Proceedings of the 2014 Zone 1 Conference of the American Society for Engineering Education
No full textIsaac Macwan (with Zihe Zhao, Omar T. Sobh, and Prabir K. Patra) is a contributing author, Magnetotaxis for Nanofabrication.https://digitalcommons.fairfield.edu/engineering-books/1054/thumbnail.jp
Erratum for “Protective effect of quercetin on bupivacaineinduced neurotoxicity via T-type calcium channel inhibition”
No full textJin et al Trop J Pharm Res 2017, 16(8): 1827-1833 http://dx.doi.org/10.4314/tjpr.v16i8.11The correct name of the First Author is Zhao as provided above and not Chao earlier published.Citation: Jin Z, Wu H, Tang C, Ke J, Wang Y. Protective effect of quercetin on bupivacaineinduced neurotoxicity via T-type calcium channel inhibition. Trop J Pharm Res 2017; 16(8):1827-1833 Erratum: 2017; 16(9):2051 http://dx.doi.org/10.4314/tjpr.v16i9.
Maximal estimates for the Weyl sums on (with an appendix by Alex Barron)
No full textIn this paper, we obtain the maximal estimate for the Weyl sums on the torus
with , which is sharp up to the endpoint. We also
consider two variants of this problem which include the maximal estimate along
the rational lines and on the generic torus. Applications, which include some
new upper bound on the Hausdorff dimension of the sets associated to the large
value of the Weyl sums, reflect the compound phenomenon between the square root
cancellation and the constructive interference. In the Appendix, an alternate
proof of Theorem 1.1 inspired by Baker's argument in [1] is given by Barron,
which also improves the loss in Theorem 1.1, and the
Strichartz-type estimates for the Weyl sums with logarithmic losses are
obtained by the same argument.Comment: 30 pages. In the new version, an appendix by Alex Barron has been
added, which gives another new proof of the main resul
In defence of psychometric measurement: a systematic review of contemporary self-report feedback inventories
No full textthe documents here are the author versions of a paper published in Educational Psychologist. The supplementary files show studies that did not qualify for inclusion in the review. The paper can be found at:
Brown, G. T. L., & Zhao, A. (2023). In defence of psychometric measurement: A systematic review of contemporary self-report feedback inventories. Educational Psychologist. https://doi.org/10.1080/00461520.2023.2208670
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Exploiting knowledge of immune selection in HIV-1 to detect HIV-specific CD8 T-cell responses
No full textSince HLA-restricted cytotoxic T-cell responses select specific polymorphisms in HIV-1 sequences and HLA diversity is relatively static in human populations, we investigated the use of peptide epitopes based on sites of HLA-associated adaptation in HIV-1 sequences to stimulate and detect T-cell responses ex vivo. These "HLA-optimised" peptides captured more HIV-1 Nef-specific responses compared with overlapping peptides of a single consensus sequence, in interferon-γ enzyme linked immunospot assays. Sites of immune selection can reveal more immunogenic epitopes in HLA-diverse populations and offer insights into the nature of HLA-epitope targeting, which could be applied in vaccine design
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