title: WASSA-2017 shared task on emotion intensity

authors: Mohmmad, Saif M.

subjects: computer science

abstract: We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best–worst scaling (BWS). We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity). The data was partitioned into training, development, and test sets for the competition. Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores. We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task. The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language.

text: WASSA-2017 shared task on emotion intensity We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best–worst scaling (BWS). We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity). The data was partitioned into training, development, and test sets for the competition. Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores. We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task. The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language.

year: 2017

title: Mobility aids detection using Convolution Neural Network (CNN)

authors: Mukhtar, Amir

subjects: convolutional neural network

abstract: The automated detection of disabled persons in surveillance videos to gain data for lobbying access for disabled persons is a largely unexplored application. We train You Only Look Once (YOLO) CNN on a custom database and achieve an accuracy of 92% for detecting disabled pedestrians in surveillance videos. A person is declared disabled if they are detected in the close proximity of a mobility aid. The detection outcome was further categorised into five classes of mobility aids and precision was calculated.

text: Mobility aids detection using Convolution Neural Network (CNN) The automated detection of disabled persons in surveillance videos to gain data for lobbying access for disabled persons is a largely unexplored application. We train You Only Look Once (YOLO) CNN on a custom database and achieve an accuracy of 92% for detecting disabled pedestrians in surveillance videos. A person is declared disabled if they are detected in the close proximity of a mobility aid. The detection outcome was further categorised into five classes of mobility aids and precision was calculated.

year: 2018

title: Mobile app development: Work-integrated learning collaborations with Māori and Fijian partners

authors: Yeo, Alvin

subjects: computer science

abstract: Only a few information and communication technology (ICT) work-integrated learning (WIL) projects involving universities and Indigenous partners are being reported and very little is known about the approaches that are followed. This article reports on successful student work placements in Indigenous communities when codeveloping software for the community. We provide two case studies of projects which involved work-integrated learning students and researchers from a university, as well as researchers and practitioners from Indigenous communities in New Zealand and Fiji. Two independent app development projects were the central focus of the collaborations with these two communities including placement students. The article describes the learning and insights from these WIL projects and provides recommendations for creating successful WIL opportunities with Indigenous communities.

text: Mobile app development: Work-integrated learning collaborations with Māori and Fijian partners Only a few information and communication technology (ICT) work-integrated learning (WIL) projects involving universities and Indigenous partners are being reported and very little is known about the approaches that are followed. This article reports on successful student work placements in Indigenous communities when codeveloping software for the community. We provide two case studies of projects which involved work-integrated learning students and researchers from a university, as well as researchers and practitioners from Indigenous communities in New Zealand and Fiji. Two independent app development projects were the central focus of the collaborations with these two communities including placement students. The article describes the learning and insights from these WIL projects and provides recommendations for creating successful WIL opportunities with Indigenous communities.

year: 2022