scholarly journals Hidden Worlds

IDEA JOURNAL ◽  
2020 ◽  
Vol 17 (02) ◽  
pp. 275-288
Author(s):  
J Rosenbaum
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Image Recognition ◽  
Learning Systems ◽  
Transgender Identity ◽  
Masculinity And Femininity ◽  
Recognition Algorithms ◽  
Difficult Time ◽  
Gender Minorities ◽  
The Way

This art project examines non-binary and transgender identity through training machines to generate art based on Greek and Roman statuary. The statuary is binary in nature and appeals to the concept of pinnacles of masculinity and femininity but what of those of us who fall between, what of transgender bodies, gender non-conforming and non-binary bodies and intersex bodies?  Image recognition algorithms have a difficult time classifying people who fall outside the binary, those who don’t pass as cisgender and those who present in neutral or subversive ways. As image recognition becomes more prevalent, we need to have a past and a future for everyone who doesn’t fit neatly into one of the only two boxes on offer. We need to open up the categories, allow people to self-identify or to scrap the concept of gendering people mechanically all together. As a spatial installation, Hidden Worlds also explores the embodiment of interactive augmented reality bodies in the space between physical and digital worlds. I have worked with a classifier and some deliberately abstract figure works, generated by machine, to explore where gender is assigned in the process and what it looks like when you aren’t neatly classified, and the disconnect that is felt when misgendered. The generated captions have flipped around gender and as the figure resolves and each section is submitted to the narrative writer you see a different set of pronouns, a disconnection between what you see and what you hear. I will explore the assumptions we make about classical art; the way it can inform how we represent gender minorities going forward and how art can illustrate the gaps that exist in the training of these important machine learning systems.

scholarly journals Explainable AI: A Review of Machine Learning Interpretability Methods

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 18
Author(s):  
Pantelis Linardatos ◽  
Vasilis Papastefanopoulos ◽  
Sotiris Kotsiantis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Black Box ◽  
Learning Systems ◽  
Model Complexity ◽  
Learning Models ◽  
New Methods ◽  
Industrial Adoption ◽  
Machine Learning Models ◽  
The Way

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.

scholarly journals Diagnosing Gender Bias in Image Recognition Systems

2020 ◽  
Vol 6 ◽  
pp. 237802312096717
Author(s):  
Carsten Schwemmer ◽  
Carly Knight ◽  
Emily D. Bello-Pardo ◽  
Stan Oklobdzija ◽  
Martijn Schoonvelde ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gender Bias ◽  
Image Recognition ◽  
Gender Stereotypes ◽  
Expert Knowledge ◽  
Physical Appearance ◽  
Past Research ◽  
Learning Systems ◽  
Gender Biases ◽  
Recognition Systems

Image recognition systems offer the promise to learn from images at scale without requiring expert knowledge. However, past research suggests that machine learning systems often produce biased output. In this article, we evaluate potential gender biases of commercial image recognition platforms using photographs of U.S. members of Congress and a large number of Twitter images posted by these politicians. Our crowdsourced validation shows that commercial image recognition systems can produce labels that are correct and biased at the same time as they selectively report a subset of many possible true labels. We find that images of women received three times more annotations related to physical appearance. Moreover, women in images are recognized at substantially lower rates in comparison with men. We discuss how encoded biases such as these affect the visibility of women, reinforce harmful gender stereotypes, and limit the validity of the insights that can be gathered from such data.

scholarly journals Synthetic heparan sulfate standards and machine learning facilitate the development of solid-state nanopore analysis

2021 ◽  
Vol 118 (11) ◽  
pp. e2022806118
Author(s):  
Ke Xia ◽  
James T. Hagan ◽  
Li Fu ◽  
Brian S. Sheetz ◽  
Somdatta Bhattacharya ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solid State ◽  
Nucleic Acid ◽  
Heparan Sulfate ◽  
Image Recognition ◽  
Single Molecule ◽  
Monosaccharide Composition ◽  
Minimal Representation ◽  
Recognition Algorithms ◽  
Potential Use

The application of solid-state (SS) nanopore devices to single-molecule nucleic acid sequencing has been challenging. Thus, the early successes in applying SS nanopore devices to the more difficult class of biopolymer, glycosaminoglycans (GAGs), have been surprising, motivating us to examine the potential use of an SS nanopore to analyze synthetic heparan sulfate GAG chains of controlled composition and sequence prepared through a promising, recently developed chemoenzymatic route. A minimal representation of the nanopore data, using only signal magnitude and duration, revealed, by eye and image recognition algorithms, clear differences between the signals generated by four synthetic GAGs. By subsequent machine learning, it was possible to determine disaccharide and even monosaccharide composition of these four synthetic GAGs using as few as 500 events, corresponding to a zeptomole of sample. These data suggest that ultrasensitive GAG analysis may be possible using SS nanopore detection and well-characterized molecular training sets.

scholarly journals Diagnosing Gender Bias in Image Recognition Systems

2018 ◽  
Author(s):  
Carsten Schwemmer ◽  
Carly Knight ◽  
Emily Bello-Pardo ◽  
Stan Oklobdzija ◽  
Martijn Schoonvelde ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gender Bias ◽  
Image Recognition ◽  
Gender Stereotypes ◽  
Expert Knowledge ◽  
Physical Appearance ◽  
Past Research ◽  
Learning Systems ◽  
Gender Biases ◽  
Recognition Systems

Image recognition systems offer the promise to learn from images at scale without requiring expert knowledge. However, past research suggests that machine learning systems often produce biased output. In this article, we evaluate potential gender biases of commercial image recognition platforms using photographs of U.S. members of Congress and a large number of Twitter images posted by these politicians. Our crowdsourced validation shows that commercial image recognition systems can produce labels that are correct and biased at the same time as they selectively report a subset of many possible true labels. We find that images of women received three times more annotations related to physical appearance. Moreover, women in images are recognized at substantially lower rates in comparison with men. We discuss how encoded biases such as these affect the visibility of women, reinforce harmful gender stereotypes, and limit the validity of the insights that can be gathered from such data.

scholarly journals Foreword

2017 ◽  
Vol 6 (4) ◽  
pp. 4
Author(s):  
Manuela EPURE
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Life ◽  
Working Life ◽  
Learning Systems ◽  
Complex Problems ◽  
Digital World ◽  
New Knowledge ◽  
The Way

Digital world is changing constantly and the way in which it affects our life and work seems to be dramatically and somehow unpredictable. New skills and competencies are required and more than ever our learning efforts must be equally distributed between old, traditional and new knowledge, more abundant and diverse far from what you learned before and beyond any imagination a decade ago.Performance at work is redefined in such a way that has no connection at all with what was expected from us at the beginning of our working life. We have been trained to use our knowledge to solve problems, now we need to be trained to use machine learning systems to deal with complex problems and to relay on artificial intelligence when it comes about understanding our digital world and his diverse connectivity with our real life.....

Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

2018 ◽  
Vol 12 ◽  
pp. 85-98
Author(s):  
Bojan Kostadinov ◽  
Mile Jovanov ◽  
Emil STANKOV
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Educational Policy ◽  
Learning Systems ◽  
Data Sources ◽  
Or Education ◽  
Student Activity ◽  
The World ◽  
E Learning ◽  
Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

Cute Girls, Tough Boys

2014 ◽  
Vol 7 (1) ◽  
pp. 85-111
Author(s):  
Alexandra Gueydan-Turek
Keyword(s):  
Gender Identity ◽  
Social Norms ◽  
Graphic Art ◽  
Masculinity And Femininity ◽  
Gender Performances ◽  
Primary Focus ◽  
Form Of Expression ◽  
The Way

This article explores the way in which masculinity and femininity are constructed in Algerian manga, an emerging, understudied sub-genre within the field of Algerian graphic art. Through the exploration of youth-oriented publications of shōjo and shōnen manga, I will demonstrate how these new local works offer a privileged form of expression for and platform to address disaffected Algerian youths. The primary focus of this investigation will be the differences (or lack thereof) between ideals of gender performances as expressed in Algerian manga and ideals of gender identity in society at large. This article will demonstrate that, while some differences manifest a desire for change on the part of both artists and readers, they certainly do not constitute radical revisions of the popular Algerian notions of masculinity and femininity. Ultimately, this study will demonstrate the limits of manga as an imported genre within an Arab-Islamic context, oscillating between the promulgation of alternative social ideals and the reinforcement of social norms.

A Fitting Solution

2018 ◽  
Author(s):  
Kaori Kashimura ◽  
Takafumi Kawasaki Jr. ◽  
Nozomi Ikeya ◽  
Dave Randall
Keyword(s):  
Organizational Change ◽  
Power Plant ◽  
Augmented Reality ◽  
Real World ◽  
Work Practices ◽  
Plant Construction ◽  
Tightly Coupled ◽  
Complex Scenario ◽  
The Relationship ◽  
The Way

This chapter provides an ethnography of a complex scenario involving the construction of a power plant and, in so doing, tries to show the importance of a practice-based approach to the problem of technical and organizational change. The chapter reports on fieldwork conducted in a highly complex and tightly coupled environment: power plant construction. The ethnography describes work practices on three different sites and describes and analyses their interlocking dependencies, showing the difficulties encountered at each location and the way in which the delays that result cascade through the different sites. It goes on to describe some technological solutions that are associated with augmented reality and that are being designed in response to the insights gained from the fieldwork. The chapter also reflects more generally on the relationship between fieldwork and design in real-world contexts.

scholarly journals Machine Learning for Communications

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 831
Author(s):  
Vaneet Aggarwal
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Video Streaming ◽  
Communication Networks ◽  
Data Analytics ◽  
Data Traffic ◽  
Traffic Demand ◽  
Performance Requirements

Due to the proliferation of applications and services that run over communication networks, ranging from video streaming and data analytics to robotics and augmented reality, tomorrow’s networks will be faced with increasing challenges resulting from the explosive growth of data traffic demand with significantly varying performance requirements [...]

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