scholarly journals Statistical and Machine Learning Analysis of Impact of Population and Gender Effect in GDP of Bangladesh: A Case Study

2020 ◽  
Vol 12 (1) ◽  
pp. 24-33
Author(s):  
Rayhan Ahmed ◽  
◽  
Ashfaq Ali Shafin
Keyword(s):  
Machine Learning ◽  
Gender Effect ◽  
And Gender ◽  
Learning Analysis

scholarly journals Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5022
Author(s):  
Francesco Asci ◽  
Giovanni Costantini ◽  
Pietro Di Leo ◽  
Alessandro Zampogna ◽  
Giovanni Ruoppolo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Experimental Studies ◽  
Combined Effect ◽  
Support Vector ◽  
Ecological Value ◽  
Age Related ◽  
And Gender ◽  
Age Range ◽  
Learning Analysis

Background: Experimental studies using qualitative or quantitative analysis have demonstrated that the human voice progressively worsens with ageing. These studies, however, have mostly focused on specific voice features without examining their dynamic interaction. To examine the complexity of age-related changes in voice, more advanced techniques based on machine learning have been recently applied to voice recordings but only in a laboratory setting. We here recorded voice samples in a large sample of healthy subjects. To improve the ecological value of our analysis, we collected voice samples directly at home using smartphones. Methods: 138 younger adults (65 males and 73 females, age range: 15–30) and 123 older adults (47 males and 76 females, age range: 40–85) produced a sustained emission of a vowel and a sentence. The recorded voice samples underwent a machine learning analysis through a support vector machine algorithm. Results: The machine learning analysis of voice samples from both speech tasks discriminated between younger and older adults, and between males and females, with high statistical accuracy. Conclusions: By recording voice samples through smartphones in an ecological setting, we demonstrated the combined effect of age and gender on voice. Our machine learning analysis demonstrates the effect of ageing on voice.

scholarly journals Reconstructing rock art chronology with transfer learning: A case study from Arnhem Land, Australia

2021 ◽  
Author(s):  
Jarrad Kowlessar ◽  
James Keal ◽  
Daryl Wesley ◽  
Ian Moffat ◽  
Dudley Lawrence ◽  
...  
Keyword(s):  
Machine Learning ◽  
Material Culture ◽  
Transfer Learning ◽  
Rock Art ◽  
Training Data ◽  
Data Sets ◽  
Learning Approaches ◽  
Arnhem Land ◽  
Learning Analysis

In recent years, machine learning approaches have been used to classify and extract style from media and have been used to reinforce known chronologies from classical art history. In this work we employ the first ever machine learning analysis of Australian rock art using a data efficient transfer learning approach to identify features suitable for distinguishing styles of rock art. These features are evaluated in a one-shot learning setting. Results demonstrate that known Arnhem Land Rock art styles can be resolved without knowledge of prior groupings. We then analyse the activation space of learned features and report on the relationships between styles and arrange these classes into a stylistic chronology based on distance within the activation space. By generating a stylistic chronology, it is shown that the model is sensitive to both temporal and spatial patterns in the distribution of rock art in the Arnhem Land Plateau region. More broadly, this approach is ideally suited to evaluating style within any material culture assemblage and overcomes the common constraint of small training data sets in archaeological machine learning studies.

The Category Is . . . Opulence!

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Joseph Plaster
Keyword(s):  
Performance Studies ◽  
Knowledge Creation ◽  
People Of Color ◽  
Gender Nonconforming ◽  
Johns Hopkins University ◽  
The Cross ◽  
And Gender ◽  
A Performance

In recent years there has been a strong “public turn” within universities that is renewing interest in collaborative approaches to knowledge creation. This article draws on performance studies literature to explore the cross-disciplinary collaborations made possible when the academy broadens our scope of inquiry to include knowledge produced through performance. It takes as a case study the “Peabody Ballroom Experience,” an ongoing collaboration between the Johns Hopkins University Sheridan Libraries, the Peabody Institute BFA Dance program, and Baltimore’s ballroom community—a performance-based arts culture comprising gay, lesbian, queer, transgender, and gender-nonconforming people of color.

The Complexity of Work for Women who are HIV Positive: A Case Study in DKI Jakarta

2018 ◽  
Vol 23 (4) ◽  
pp. 203
Author(s):  
Andi Nur Faizah
Keyword(s):  
Qualitative Method ◽  
Family Members ◽  
Hiv Positive ◽  
Difficult Situation ◽  
Women Living With Hiv ◽  
Hiv Positive Women ◽  
And Gender ◽  
Gender Categories ◽  
Living With Hiv

<p>The phenomenon of HIV-AIDS transmission places women in a difficult situation. The loss of family members such as husbands due to AIDS leaves women living with HIV positive in a struggle to access sources of livelihood. The condition of themselves as PLWHA, concerns about being stigmatized, caring for family members, and earning a living are the burdens of life they have to face. In this regard, this paper explores the complexity of the work of HIV-positive women. This study uses a qualitative method with a feminist perspective to get a complete picture of the livelihood of HIV-positive women. Based on interviews with five HIV-positive women, the findings found a link between social, identity, and gender categories that affect their livelihoods. HIV-positive women also transform themselves into their “normal” self by pretending to be healthy, able to work, have quality, and be independent. This is done as a form of resistance to the stigma attached to PLWHA.</p><p> </p><p> </p>

Race and Gender

2020 ◽  
pp. 251-269 ◽  
Author(s):  
Timnit Gebru
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
The United States ◽  
Error Rates ◽  
Political Factors ◽  
Recidivism Rates ◽  
Race And Gender ◽  
Decision Tools ◽  
And Gender ◽  
Technical Solutions

This chapter discusses the role of race and gender in artificial intelligence (AI). The rapid permeation of AI into society has not been accompanied by a thorough investigation of the sociopolitical issues that cause certain groups of people to be harmed rather than advantaged by it. For instance, recent studies have shown that commercial automated facial analysis systems have much higher error rates for dark-skinned women, while having minimal errors on light-skinned men. Moreover, a 2016 ProPublica investigation uncovered that machine learning–based tools that assess crime recidivism rates in the United States are biased against African Americans. Other studies show that natural language–processing tools trained on news articles exhibit societal biases. While many technical solutions have been proposed to alleviate bias in machine learning systems, a holistic and multifaceted approach must be taken. This includes standardization bodies determining what types of systems can be used in which scenarios, making sure that automated decision tools are created by people from diverse backgrounds, and understanding the historical and political factors that disadvantage certain groups who are subjected to these tools.

Case Study: The Southern Oral History Program

2012 ◽  
Author(s):  
Jacquelyn Dowd Hall ◽  
Kathryn Nasstrom
Keyword(s):  
Oral History ◽  
American South ◽  
New Era ◽  
Chapel Hill ◽  
Integral Role ◽  
The American South ◽  
And Gender ◽  
History Department ◽  
The University

A case study of the southern oral history program is the essence of this chapter. From its start in 1973 until 1999, the Southern Oral History Program (SOHP) was housed by the history department at the University of North Carolina at Chapel Hill (UNC), rather than in the library or archives, where so many other oral history programs emerged. The SOHP is now part of UNC's Center for the Study of the American South, but it continues to play an integral role in the department of history. Concentrating on U.S. southern racial, labor, and gender issues, the program offers oral history courses and uses interviews to produce works of scholarship, such as the prize-winning book Like a Family: The Making of a Southern Cotton Mill World. The folks at the Institute for Southern Studies tried to combine activism with analysis, trying to figure out how to take the spirit of the movement into a new era.

scholarly journals ‘Women take care and men take charge’: The case of leadership and gender in the Public and Commercial Services Union

2020 ◽  
pp. 0143831X2094368
Author(s):  
Julie Prowse ◽  
Peter Prowse ◽  
Robert Perrett
Keyword(s):  
Leadership Style ◽  
Trade Unions ◽  
Union Leader ◽  
The Public ◽  
Union Leadership ◽  
Positive Attitudes ◽  
Union Leaders ◽  
And Gender ◽  
Survey Responses

This article presents the findings of a case study that aimed to understand the specific leadership styles that are valued by women and men lay representatives in the Public and Commercial Services Union (PCS) and to determine the gendered implications for increasing women’s leadership and representation in trade unions. Survey responses from PCS lay representatives (reps) show the majority of women and men agreed that the leadership style they value, and that makes a good union leader, is post-heroic (communal) leadership. This approach is associated with leadership characteristics such as being helpful, sensitive and kind and are generally practised by women. This contrasts with male union leaders who are associated with a traditional, heroic (agentic) leadership style characterised by confidence, self-reliance and decisiveness. Although some differences exist that highlight gender issues, both women and men lay reps have positive attitudes towards increasing women’s representation and participation in union leadership.

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