scholarly journals Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference

2021 ◽  
Vol 109 (4) ◽  
Author(s):  
Paul Sebo
Keyword(s):  
Performance Metrics ◽  
Data Manipulation ◽  
Detection Tool ◽  
Overall Performance ◽  
And Gender ◽  
Gender Detection ◽  
Simple Manipulation

Objective: We recently showed that genderize.io is not a sufficiently powerful gender detection tool due to a large number of nonclassifications. In the present study, we aimed to assess whether the accuracy of inference by genderize.io can be improved by manipulating the first names in the database.Methods: We used a database containing the first names, surnames, and gender of 6,131 physicians practicing in a multicultural country (Switzerland). We uploaded the original CSV file (file #1), the file obtained after removing all diacritic marks, such as accents and cedilla (file #2), and the file obtained after removing all diacritic marks and retaining only the first term of the compound first names (file #3). For each file, we computed three performance metrics: proportion of misclassifications (errorCodedWithoutNA), proportion of nonclassifications (naCoded), and proportion of misclassifications and nonclassifications (errorCoded).Results: naCoded, which was high for file #1 (16.4%), was reduced after data manipulation (file #2: 11.7%, file #3: 0.4%). As the increase in the number of misclassifications was small, the overall performance of genderize.io (i.e., errorCoded) improved, especially for file #3 (file #1: 17.7%, file #2: 13.0%, and file #3: 2.3%).Conclusions: A relatively simple manipulation of the data improved the accuracy of gender inference by genderize.io. We recommend using genderize.io only with files that were modified in this way.

Age and Gender Detection System using Raspberry Pi

2019 ◽  
Vol 7 (6) ◽  
pp. 14-18
Author(s):  
Sumangala Biradar ◽  
Beena Torgal ◽  
Namrata Hosamani ◽  
Renuka Bidarakundi ◽  
Shruti Mudhol
Keyword(s):  
Detection System ◽  
Raspberry Pi ◽  
Age And Gender ◽  
And Gender ◽  
Gender Detection

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

scholarly journals Disentangling general achievement levels and subject-specific strengths and weaknesses in mathematics, reading, and science

2020 ◽  
Author(s):  
Olesya Gladushyna ◽  
Rolf Strietholt ◽  
Isa Steinmann
Keyword(s):  
Reading Literacy ◽  
Performance Levels ◽  
Fourth Grade Students ◽  
Subject Specific ◽  
The Subject ◽  
Overall Performance ◽  
Cross Country ◽  
And Gender ◽  
Mathematics And Science ◽  
Achievement Levels

AbstractThe paper uses data from the combined TIMSS (Trends in International Mathematics and Science Study) and PIRLS (Progress in International Reading Literacy Study) assessment in 2011 to explore the subject-specific strengths and weaknesses among fourth grade students worldwide. Previous research came to the conclusion that students only differed in overall achievement levels and did not exhibit subject-specific strengths and weaknesses. This research did, however, not control for differences in overall performance levels when searching for profile differences. Therefore, the present study uses factor mixture analysis to study qualitatively different performance profiles in mathematics, reading, and science while controlling for differences in performance levels. Our findings suggest that the majority of students do not show pronounced strengths and weaknesses and differ mainly in performance levels across mathematics, reading, and science. At the same time, a smaller share of students does indeed show pronounced subject-specific strengths and weaknesses. This result does not represent an artefact, but we find clear and theory-conforming associations between the identified profiles and covariates. We find evidence for cross-country differences in the frequency of subject-specific strengths and weaknesses and gender differences, as well as differences between students who do not or only sometimes speak the language of test at home.

On the Performance Comparisons of Native and Clientless Real-Time Screen-Sharing Technologies

2021 ◽  
Vol 17 (2) ◽  
pp. 1-26
Author(s):  
Chun-ying Huang ◽  
Yun-chen Cheng ◽  
Guan-zhang Huang ◽  
Ching-ling Fan ◽  
Cheng-hsin Hsu
Keyword(s):  
Real Time ◽  
Computer Games ◽  
Performance Metrics ◽  
Video Quality ◽  
Dynamic Network ◽  
Web Browsers ◽  
Depth Analysis ◽  
Measurement Methodology ◽  
Overall Performance ◽  
Screen Sharing

Real-time screen-sharing provides users with ubiquitous access to remote applications, such as computer games, movie players, and desktop applications (apps), anywhere and anytime. In this article, we study the performance of different screen-sharing technologies, which can be classified into native and clientless ones. The native ones dictate that users install special-purpose software, while the clientless ones directly run in web browsers. In particular, we conduct extensive experiments in three steps. First, we identify a suite of the most representative native and clientless screen-sharing technologies. Second, we propose a systematic measurement methodology for comparing screen-sharing technologies under diverse and dynamic network conditions using different performance metrics. Last, we conduct extensive experiments and perform in-depth analysis to quantify the performance gap between clientless and native screen-sharing technologies. We found that our WebRTC-based implementation achieves the best overall performance. More precisely, it consumes a maximum of 3 Mbps bandwidth while reaching a high decoding ratio and delivering good video quality. Moreover, it leads to a steadily high decoding ratio and video quality under dynamic network conditions. By presenting the very first rigorous comparisons of the native and clientless screen-sharing technologies, this article will stimulate more exciting studies on the emerging clientless screen-sharing technologies.

Muscle Fiber Typology and Its Association With Start and Turn Performance in Elite Swimmers

2020 ◽  
pp. 1-7
Author(s):  
Adam Mallett ◽  
Phillip Bellinger ◽  
Wim Derave ◽  
Eline Lievens ◽  
Ben Kennedy ◽  
...  
Keyword(s):  
Muscle Fiber ◽  
Control Group ◽  
Resonance Spectroscopy ◽  
Start Time ◽  
Racing Performance ◽  
Elite Swimmers ◽  
National Competition ◽  
Overall Performance ◽  
And Gender ◽  
Performance Times

Purpose: To determine the association between estimated muscle fiber typology and the start and turn phases of elite swimmers during competition. Methods: International and national competition racing performance was analyzed from 21 female (FINA points = 894 ± 39: 104.5 ± 1.8% world record ratio [WRR]) and 25 male (FINA points = 885 ± 54: 104.8 ± 2.1% WRR) elite swimmers. The start, turn, and turn out times were determined from each of the swimmers’ career best performance times (FINA points = 889 ± 48: 104.7 ± 2.0% WRR). Muscle carnosine concentration was quantified by proton magnetic resonance spectroscopy in the gastrocnemius and soleus and was expressed as a carnosine aggregate z score relative to an age- and gender-matched nonathlete control group to estimate muscle fiber typology. Linear mixed models were employed to determine the association between muscle fiber typology and the start and turn times. Results: While there was no significant influence of carnosine aggregate z score on the start and turn times when all strokes and distance events were entered into the model, the swimmers with a higher carnosine aggregate z score (ie, faster muscle typology) had a significantly faster start time in 100-m events compared with the swimmers with a lower carnosine aggregate z score (P = .02, F = 5.825). The start and turn times were significantly faster in the male compared with the female swimmers in the 100-m events compared with other distances, and between the 4 different swimming strokes (P < .001). Conclusion: This study suggests that start times in sprint events are partly determined (and limited) by muscle fiber typology, which is highly relevant when ∼12% of the overall performance time is determined from the start time.

The Case for Group Heterogeneity

2017 ◽  
pp. 1852-1871
Author(s):  
Teigan Margetts ◽  
Elise Holland
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Gender Diversity ◽  
Top Management Teams ◽  
Decision Making Process ◽  
Gender Perspective ◽  
Dichotomous Variable ◽  
Group Heterogeneity ◽  
Overall Performance ◽  
And Gender

In this chapter, the case for group heterogeneity in organisations, from a gender perspective, will be put forward. The chapter will argue that diversity within top management teams (TMTs) and boards is necessary to ensure the proper functioning of the group decision-making process, and also to increase innovation across the firm. Whilst gender is not a dichotomous variable, females do bring a level of difference to any given group. For decision-making, this level of difference is critical to prevent groupthink, cascades and polarisation. The chapter will also argue that ‘diversity' cannot mean the ‘token' introduction of one female, rather, diversity is most valuable when equality is achieved. Given that incremental increases are associated with diversity for innovation, the chapter will also argue that equality leads to better innovation. Overall, the chapter will aim to demonstrate that group heterogeneity and gender diversity results in better decisions, better innovations, and better overall performance for organisations.

Anticipation of Demand in Supply Chains

2019 ◽  
pp. 1-45
Author(s):  
Youssef Tliche ◽  
Atour Taghipour ◽  
Béatrice Canel-Depitre
Keyword(s):  
Supply Chains ◽  
Information Sharing ◽  
Performance Metrics ◽  
Mean Squared Error ◽  
Moving Average ◽  
Demand Management ◽  
Inventory Level ◽  
Squared Error ◽  
Interfirm Collaboration ◽  
Overall Performance

The main objective of studying decentralized supply chains is to demonstrate that a better interfirm collaboration can lead to a better overall performance of the system. Many researchers studied a phenomenon called downstream demand inference (DDI), which presents an effective demand management strategy to deal with forecast problems. DDI allows the upstream actor to infer the demand received by the downstream one without information sharing. Recent study showed that DDI is possible with simple moving average (SMA) forecast method and was verified especially for an autoregressive AR(1) demand process. This chapter extends the strategy's results by developing mean squared error and average inventory level expressions for causal invertible ARMA(p,q) demand under DDI strategy, no information sharing (NIS), and forecast information sharing (FIS) strategies. The authors analyze the sensibility of the performance metrics in respect with lead-time, SMA, and ARMA(p,q) parameters, and compare DDI results with the NIS and FIS strategies' results.

Application of Cluster Analysis to Climate Model Performance Metrics

2011 ◽  
Vol 50 (8) ◽  
pp. 1666-1675 ◽  
Author(s):  
Satoru Yokoi ◽  
Yukari N. Takayabu ◽  
Kazuaki Nishii ◽  
Hisashi Nakamura ◽  
Hirokazu Endo ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
General Circulation ◽  
Performance Metrics ◽  
Climate Model ◽  
Model Performance ◽  
General Circulation Models ◽  
Clustering Methods ◽  
Dynamic Constraints ◽  
Overall Performance

AbstractThe overall performance of general circulation models is often investigated on the basis of the synthesis of a number of scalar performance metrics of individual models that measure the reproducibility of diverse aspects of the climate. Because of physical and dynamic constraints governing the climate, a model’s performance in simulating a certain aspect of the climate is sometimes related closely to that in simulating another aspect, which results in significant intermodel correlation between performance metrics. Numerous metrics and intermodel correlations may cause a problem in understanding the evaluation and synthesizing the metrics. One possible way to alleviate this problem is to group the correlated metrics beforehand. This study attempts to use simple cluster analysis to group 43 performance metrics. Two clustering methods, the K-means and the Ward methods, yield considerably similar clustering results, and several aspects of the results are found to be physically and dynamically reasonable. Furthermore, the intermodel correlation between the cluster averages is considerably lower than that between the metrics. These results suggest that the cluster analysis is helpful in obtaining the appropriate grouping. Applications of the clustering results are also discussed.

scholarly journals Age and Gender Detection Using CNN

2021 ◽  
pp. 29-33
Author(s):  
Prof. Jaydeep Patil ◽  
Rohit Thombare ◽  
Yash deo ◽  
Rohit Kharche ◽  
Nikhil Tagad
Keyword(s):  
Real World ◽  
Working Environment ◽  
Age And Gender ◽  
The Real ◽  
Lighting Conditions ◽  
And Gender ◽  
Gender Detection ◽  
World Environment ◽  
Age Estimates

In recent years, much effort has been put forth to balance age and sexuality. It has been reported that the age can be accurately measured under controlled areas such as front faces, no speech, and stationary lighting conditions. However, it is not intended to achieve the same level of accuracy in the real world environment due to the wide variation in camera use, positioning, and lighting conditions. In this paper, we use a recently proposed mechanism to study equipment called covariate shift adaptation to reduce the change in lighting conditions between the laboratory and the working environment. By examining actual age estimates, we demonstrate the usefulness of our proposed approach.

Sign in / Sign up

Export Citation Format

Share Document