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A Voice of Process: Re-Presencing the Gendered Labor of Apollo Innovation
From Ada Lovelace to Margaret Hamilton, retelling the stories of previously unrecognized women can broaden histories of technology and challenge the dominant imaginary of innovation today. These figures remind us that women can be—and always have been—part of computing. Yet, their significant accomplishments represent a small fraction of women’s contributions to technology. Women, and especially working class women of color, have consistently done the work just below the surface of discovery. However, the data comprising their experiences remains thin, keeping those figures on the scientific margins. This essay explores how communication studies can integrate expanded methods of media archeology to address issues of representation in the absence of remarkable personal narratives. We present the case study the Apollo Guidance Computer’s woven core memory, a history that is “re-presenced” through a participatory workshop that engages participants in collaborative acts of weaving. In an appeal to the tactics of design, this recuperation opens an indeterminate past to illuminate the networks of labor called into being by technological artifacts. We argue that integrating these methods can produce new, feminist histories of material practices—bringing people and places into the present along with their associated artifacts
English language readability task performance in a mobile setting - the effect of gender
Mobile computing has become very common in the present day fast changing technological development. It is expected that in future, people will be more mobile than today and all kinds of tasks that are performed in the stationary environment will be undertaken in a mobile environment also. As traffic on the road and also the population
are increasing at a very fast pace, the future generation will spend a lot of time in a mobile environment. Therefore, assessment of operators’ performance in a mobile setting will become all the more important. Mobile environment is influenced by vehicular vibration for all kinds of tasks. The present study made an attempt to explore the English language readability performance of a target group. Fourteen subjects (seven males and seven females) from an English language teaching institute were selected for this study. The base line value of reading speed was obtained on the basis of stationary environment
reading task performance. Reading speed was noted in the number of words read per minute (NWRPM). The same subjects were used for reading in the vibratory environment and difference in the performance was noticed. A stimulus was presented on a lap-top in both cases. Vibration was assessed on the basis of ISO 2631-1 (1997) guideline.
ANOVA statistical tool was used to analyze the data. The result indicated that the performance of operators was significantly affected due to the presence of vibration and
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Generative Adversarial Networks for Mitigating Biases in Machine Learning Systems
In this paper, we propose a new framework for mitigating biases in machine
learning systems. The problem of the existing mitigation approaches is that
they are model-oriented in the sense that they focus on tuning the training
algorithms to produce fair results, while overlooking the fact that the
training data can itself be the main reason for biased outcomes. Technically
speaking, two essential limitations can be found in such model-based
approaches: 1) the mitigation cannot be achieved without degrading the accuracy
of the machine learning models, and 2) when the data used for training are
largely biased, the training time automatically increases so as to find
suitable learning parameters that help produce fair results. To address these
shortcomings, we propose in this work a new framework that can largely mitigate
the biases and discriminations in machine learning systems while at the same
time enhancing the prediction accuracy of these systems. The proposed framework
is based on conditional Generative Adversarial Networks (cGANs), which are used
to generate new synthetic fair data with selective properties from the original
data. We also propose a framework for analyzing data biases, which is important
for understanding the amount and type of data that need to be synthetically
sampled and labeled for each population group. Experimental results show that
the proposed solution can efficiently mitigate different types of biases, while
at the same time enhancing the prediction accuracy of the underlying machine
learning model
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