ORCID Profile
0000-0001-9958-0102
Current Organisations
Monash University
,
Deakin University
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Publisher: Elsevier BV
Date: 07-2020
Publisher: IEEE
Date: 07-2019
Publisher: SCITEPRESS - Science and Technology Publications
Date: 2021
Publisher: IEEE
Date: 05-2018
Publisher: Elsevier BV
Date: 06-2020
Publisher: Elsevier BV
Date: 12-2021
Publisher: IEEE
Date: 03-2021
Publisher: IEEE
Date: 07-2018
Publisher: ACM
Date: 23-10-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: SCITEPRESS - Science and Technology Publications
Date: 2022
Publisher: ACM
Date: 17-05-2022
Publisher: Springer International Publishing
Date: 2021
Publisher: ACM
Date: 16-10-2020
Publisher: IEEE
Date: 06-2017
Publisher: Association for Computing Machinery (ACM)
Date: 13-10-2021
DOI: 10.1145/3479547
Abstract: Collaborative editing questions and answers plays an important role in quality control of Mathematics StackExchange which is a math Q& A Site. Our study of post edits in Mathematics Stack Exchange shows that there is a large number of math-related edits about latexifying formulas, revising LaTeX and converting the blurred math formula screenshots to LaTeX sequence. Despite its importance, manually editing one math-related post especially those with complex mathematical formulas is time-consuming and error-prone even for experienced users. To assist post owners and editors to do this editing, we have developed an edit-assistance tool, MathLatexEdit for formula latexification, LaTeX revision and screenshot transcription. We formulate this formula editing task as a translation problem, in which an original post is translated to a revised post. MathLatexEdit implements a deep learning based approach including two encoder-decoder models for textual and visual LaTeX edit recommendation with math-specific inference. The two models are trained on large-scale historical original-edited post pairs and synthesized screenshot-formula pairs. Our evaluation of MathLatexEdit not only demonstrates the accuracy of our model, but also the usefulness of MathLatexEdit in editing real-world posts which are accepted in Mathematics Stack Exchange.
Publisher: IEEE
Date: 11-2021
Publisher: IEEE
Date: 10-2011
Publisher: ACM
Date: 27-06-2020
Publisher: IEEE
Date: 05-2022
Publisher: ACM
Date: 30-11-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2022
Publisher: ACM
Date: 12-06-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Publisher: World Scientific Pub Co Pte Lt
Date: 09-2013
DOI: 10.1142/S1469026813500181
Abstract: In this paper, a hierarchical structure based convolutional neural network is proposed to provide the ability for robust information processing. The weight sharing ability of convolutional neural networks (CNNs) is considered as a level of hierarchy in these networks. Weight sharing reduces the number of free parameters and improves the generalization ability. In the proposed structure, a small CNN which is used for feature extractor is shared between the whole input image pixels. A scalable architecture for implementing extensive CNNs is resulted using a smaller and modularized trainable network to solve a large and complicated task. The proposed structure causes less training time, fewer numbers of parameters and higher test data accuracy. The recognition accuracy for recognizing unseen data shows improvement in generalization. Also presented are application ex les for face recognition. The comprehensive experiments completed on ORL, Yale and JAFFE face databases show improved classification rates and reduced training time and network parameters.
Publisher: SCITEPRESS - Science and Technology Publications
Date: 2022
Publisher: Elsevier BV
Date: 11-2021
Publisher: Springer International Publishing
Date: 21-11-2014
Publisher: Springer Science and Business Media LLC
Date: 12-01-2022
DOI: 10.1186/S13063-021-05877-3
Abstract: Increasing participation in the Australian National Bowel Cancer Screening Program (NBCSP) is the most efficient and cost-effective way of reducing mortality associated with colorectal cancer by detecting and treating early-stage disease. Currently, only 44% of Australians aged 50–74 years complete the NBCSP. This efficacy trial aims to test whether this SMS intervention is an effective method for increasing participation in the NBCSP. Furthermore, a process evaluation will explore the barriers and facilitators to sending the SMS from general practice. We will recruit 20 general practices in the western region of Victoria, Australia to participate in a cluster randomised controlled trial. General practices will be randomly allocated with a 1:1 ratio to either a control or intervention group. Established general practice software will be used to identify patients aged 50 to 60 years old who are due to receive a NBCSP kit in the next month. The SMS intervention includes GP endorsement and links to narrative messages about the benefits of and instructions on how to complete the NBCSP kit. It will be sent from intervention general practices to eligible patients prior to receiving the NBCSP kit. We require 1400 eligible patients to provide 80% power with a two-sided 5% significance level to detect a 10% increase in CRC screening participation in the intervention group compared to the control group. Our primary outcome is the difference in the proportion of eligible patients who completed a faecal occult blood test (FOBT) between the intervention and control group for up to 12 months after the SMS was sent, as recorded in their electronic medical record (EMR). A process evaluation using interview data collected from general practice staff (GP, practice managers, nurses) and patients will explore the feasibility and acceptability of sending and receiving a SMS to prompt completing a NBCSP kit. This efficacy trial will provide initial trial evidence of the utility of an SMS narrative intervention to increase participation in the NBCSP. The results will inform decisions about the need for and design of a larger, multi-state trial of this SMS intervention to determine its cost-effectiveness and future implementation. Australian New Zealand Clinical Trials Registry ACTRN12620001020976 . Registered on 17 October 2020.
Publisher: IEEE
Date: 09-2019
Publisher: ACM
Date: 23-10-2022
Publisher: Springer International Publishing
Date: 2022
Publisher: Springer International Publishing
Date: 2021
Publisher: IEEE
Date: 10-2021
Publisher: Elsevier BV
Date: 02-2022
Publisher: IEEE
Date: 08-2020
Publisher: Springer International Publishing
Date: 2022
Publisher: ACM
Date: 13-06-2022
Publisher: IEEE
Date: 05-2021
No related grants have been discovered for Hourieh Khalajzadeh.