(Fall 2012) The Effects of User Characteristics on Iris Capture

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International Center for Biometric Research

The purpose of this study was to determine if there was an impact on iris capture error rates based on various demographic groups. The three demographics which we explored in our analysis were gender, age and height. We noticed that gender and age showed significant differences in error rates. However height appeared to have no impact on error rates.

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THE EFFECTS OF USER CHARACTERISTICS ON IRIS CAPTURE
The purpose of this study was to determine if there was an impact on iris capture error rates based on various
demographic groups. The three demographics which we explored in our analysis were gender, age and height. We
noticed that gender and age showed significant differences in error rates. However height appeared to have no
impact on error rates.
Michael Porter, Andrew Strong, Mark Haworth, Michael Brockly, Stephen Elliott
Overview
Subject Pool
Gender Age
Height (in inches)
45
53
Male
Female
34
35
15
15 18-24
25-35
36-50
51-69
We used the Tukey range test to determine if there is
significant statistical correlation between demographic group
characteristics. Based on error rates, characteristics were
assigned to a group (A or B) to denote if they are statistically
different or similar. P values were less than .05 for all tables.
We found there was significant difference in error rates
between men and women overall.
We also determined that older age groups show significantly
more errors. The 51-69 age group showed very high error rates
as compared to the other groups.
We looked at the data broken down by age for each gender.
We noticed that the greatest gap of failure rates occurred in
the youngest age group (18-24). In this group, women failed
significantly more often.
We found that differences in height played no distinguishable
role in the number of errors.
Gender N Mean (Failure Rate) Grouping
F 53 8.679 A
M 45 5.044 B
Age N Mean (Failure Rate) Grouping
51-69 15 12.133 A
36-50 15 8 A B
25-35 35 4.686 B
18-24 34 6.529 B
Gender
(18-24 year olds)
N Mean (Failure
Rate)
Grouping
F 20 9.050 A
M 14 2.929 B
Results
This is an overview of the whole
population in our data set
based on age and gender. It is
clear that age does play a role
in error rates. However a larger
population could produce
different results.
It is clear from this chart that
there is no significant
correlation between height and
failure rates.
Final Thoughts
• Further studies with larger populations are necessary to verify our findings.
• A follow up study could be performed to determine if other factors affect these error rates. These factors
could include weight, environment, or test administrator.
Failure Rate Scatter Plots
35%
39%
26% 61-65
66-71
72-79
69 Subjects with and an
average height of 67.8”

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Page 1 of 1 PSY2061 Research Methods Lab © 2013 South University Steroid Usage, Grip Strength, Aggression, and Happiness Study Steroid Usage Grip Strength Aggression Happiness Investigator 3 12 7 5 1 11 49 19 7 1 23 54 30 3 1 15 19 21 6 1 19 64 24 7 2 4 20 5 10 2 19 57 19 5 2 13 21 18 11 2 24 78 36 14 3 6 15 32 11 3 14 17 4 9 3 29 87 26 5 4 2 12 8 5 4 17 74 22 6 4 4 35 6 7 4 Steroid Usage: The number entered reflects the number of weeks on steroids. Grip Strength: The number registered on a hand meter. The larger the number, the stronger the person; the scale cannot go higher than 100. Aggression: The number obtained on the McGuthry Aggression Inventory. The higher the number, the more aggression; the scale goes from 5 to 30. Happiness: The number obtained on the Happiness Scale. The higher the number, the happier the person is; the scale goes from 0 to 15. Investigator: The number assigned to the person who assessed the participant. Chapter 3, Section 2, Exercise 068ad Bisphenol A in Your Soup Cans Bisphenol A (BPA) is in the lining of most canned goods, and recent studies have shown a positive association between BPA exposure and behavior and health problems. How much does canned soup consumption increase urinary BPA concentration? That was the question addressed in a recent study1 in which consumption of canned soup over five days was associated with a more than 1000% increase in urinary BPA. In the study, 75 participants ate either canned soup or fresh soup for lunch for five days. On the fifth day, urinary BPA levels were measured. After a two-day break, the participants switched groups and repeated the process. The difference in BPA levels between the two treatments was measured for each participant. The study reports that a 95% confidence interval for the difference in means (canned minus fresh) is 19.6 to 25.5 μg/L. 1Carwile J., Ye X., Zhou X., Calafat A., and Michels K., "Canned Soup Consumption and Urinary Bisphenol A: A Randomized Crossover Trial," Journal of the American Medical Association, 2011; 306(20): 2218–2220. (a) Is this a randomized comparative experiment or a matched pairs experiment? Randomized comparative experiment Matched pairs experiment SHOW HINT LINK TO TEXT (d) If the study had included 500 participants instead of 75, would you expect the confidence interval to be wider or narrower? Wider Narrower Click if you would like to Show Work for this question: Open Show Work SHOW HINT LINK TO TEXT ter 3, Section 2, Exercise 061acd Have You Ever Been Arrested? According to a recent study of 7335 young people in the US, 30% had been arrested1 for a crime other than a traffic violation by the age of 23. Crimes included such things as vandalism, underage drinking, drunken driving, shoplifting, and drug possession. 1From a study in USA Today, quoted in The Week, 2012; 11: 547–548..

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ANRV381-SO35-23 ARI 5 June 2009 9:34 Income Inequality and Social Dysfunction Richard G. Wilkinson1 and Kate E. Pickett2 1Division of Epidemiology and Public Health, University of Nottingham, Nottingham, NG7 2UH, United Kingdom; email: [email protected] 2Department of Health Sciences, University of York, and Hull-York Medical School, York, YO10 5DD, United Kingdom; email: [email protected] Annu. Rev. Sociol. 2009. 35:493–511 First published online as a Review in Advance on April 6, 2009 The Annual Review of Sociology is online at soc.annualreviews.org This article’s doi: 10.1146/annurev-soc-070308-115926 Copyright c© 2009 by Annual Reviews. All rights reserved 0360-0572/09/0811-0493$20.00 Key Words health, social problems, social stratification, equality, relative deprivation Abstract Population health tends to be better in societies where income is more equally distributed. Recent evidence suggests that many other social problems, including mental illness, violence, imprisonment, lack of trust, teenage births, obesity, drug abuse, and poor educational performance of schoolchildren, are also more common in more unequal societies. Differences in the prevalence of ill health and social problems between more and less equal societies seem to be large and to extend to the vast majority of the population. Rather than referencing all the literature, this paper attempts to show which interpretations of these relationships are consistent with the research evidence. After discussing their more important and illuminating characteristics, we conclude that these relationships are likely to reflect a sensitivity of health and social problems to the scale of social stratification and status competition, underpinned by societal differences in material inequality. 493 A nn u. R ev . S oc io l. 20 09 .3 5: 49 3- 51 1. D ow nl oa de d fr om w w w .a nn ua lr ev ie w s. or g A cc es s pr ov id ed b y C al if or ni a St at e U ni ve rs ity - F re sn o on 1 1/ 25 /1 6. F or p er so na l u se o nl y. ANRV381-SO35-23 ARI 5 June 2009 9:34 WHAT HAS TO BE EXPLAINED Better Health in More Equal Societies The first evidence of a tendency for societies with lower income inequality to have better health came from an international cross- sectional analysis of 56 rich and poor countries. Life expectancy and infant mortality were regressed against national income per capita and the Gini coefficient of income distribution (Rodgers 1979). The paper concluded that [t]he most striking result is the consistent sig- nificance of the income distribution variable. This is a very robust conclusion which holds across a variety of specifications. . . . The results for life expectancy at birth suggest that the difference in average life expectancy between a relatively egalitarian and relatively inegalitarian country is likely to be as much as five to ten years (p. 350). There are now as many as 200 analyse.

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Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.

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Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?

Driving Behavioral Change for Information Management through Data-Driven Gree...

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This presentation explores the impact of HTML injection attacks on web applications, detailing how attackers exploit vulnerabilities to inject malicious code into web pages. Learn about the potential consequences of such attacks and discover effective mitigation strategies to protect your web applications from HTML injection vulnerabilities. for more information visit https://bostoninstituteofanalytics.org/category/cyber-security-ethical-hacking/

HTML Injection Attacks: Impact and Mitigation Strategies

Boston Institute of Analytics

Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.

Exploring the Future Potential of AI-Enabled Smartphone Processors

debabhi2

The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

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(Fall 2012) The Effects of User Characteristics on Iris Capture

1. THE EFFECTS OF USER CHARACTERISTICS ON IRIS CAPTURE The purpose of this study was to determine if there was an impact on iris capture error rates based on various demographic groups. The three demographics which we explored in our analysis were gender, age and height. We noticed that gender and age showed significant differences in error rates. However height appeared to have no impact on error rates. Michael Porter, Andrew Strong, Mark Haworth, Michael Brockly, Stephen Elliott Overview Subject Pool Gender Age Height (in inches) 45 53 Male Female 34 35 15 15 18-24 25-35 36-50 51-69 We used the Tukey range test to determine if there is significant statistical correlation between demographic group characteristics. Based on error rates, characteristics were assigned to a group (A or B) to denote if they are statistically different or similar. P values were less than .05 for all tables. We found there was significant difference in error rates between men and women overall. We also determined that older age groups show significantly more errors. The 51-69 age group showed very high error rates as compared to the other groups. We looked at the data broken down by age for each gender. We noticed that the greatest gap of failure rates occurred in the youngest age group (18-24). In this group, women failed significantly more often. We found that differences in height played no distinguishable role in the number of errors. Gender N Mean (Failure Rate) Grouping F 53 8.679 A M 45 5.044 B Age N Mean (Failure Rate) Grouping 51-69 15 12.133 A 36-50 15 8 A B 25-35 35 4.686 B 18-24 34 6.529 B Gender (18-24 year olds) N Mean (Failure Rate) Grouping F 20 9.050 A M 14 2.929 B Results This is an overview of the whole population in our data set based on age and gender. It is clear that age does play a role in error rates. However a larger population could produce different results. It is clear from this chart that there is no significant correlation between height and failure rates. Final Thoughts • Further studies with larger populations are necessary to verify our findings. • A follow up study could be performed to determine if other factors affect these error rates. These factors could include weight, environment, or test administrator. Failure Rate Scatter Plots 35% 39% 26% 61-65 66-71 72-79 69 Subjects with and an average height of 67.8”

(Fall 2012) The Effects of User Characteristics on Iris Capture

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