Buddhika Nettasinghe, Nazanin Alipourfard, Vikram Krishnamurthy
et al.
Scientific attention is unevenly distributed, creating inequities in recognition and distorting access to opportunities. Using citations as a proxy, we quantify disparities in attention by gender and institutional prestige. We find that women receive systematically fewer citations than men, and that attention is increasingly concentrated among authors from elite institutions -- patterns not fully explained by underrepresentation alone. To explain these dynamics, we introduce a model of citation network growth that incorporates homophily (tendency to cite similar authors), preferential attachment (favoring highly cited authors) and group size (underrepresentation). The model shows that disparities arise not only from group size imbalances but also from cumulative advantage amplifying biased citation preferences. Importantly, increasing representation alone is often insufficient to reduce disparities. Effective strategies should also include reducing homophily, amplifying the visibility of underrepresented groups, and supporting equitable integration of newcomers. Our findings highlight the challenges of mitigating inequities in asymmetric networks like citations, where recognition flows in one direction. By making visible the mechanisms through which attention is distributed, we contribute to efforts toward a more responsible web of science that is fairer, more transparent, and more inclusive, and that better sustains innovation and knowledge production.
Matteo Milazzo, Federico Musciotto, Jyrki Piilo
et al.
We study the long term dynamics of the stock portfolios owned by single Finnish legal entities in the Helsinki venue of the Nasdaq Nordic between 2001 and 2021. Using the Herfindahl-Hirschman index as a measure of concentration for the composition of stock portfolios, we investigate the concentration of Finnish household portfolios both at the level of each individual household and tracking the time evolution of an aggregated Finnish household portfolio. We also consider aggregated portfolios of two other macro categories of investors one comprising Finnish institutional investors and the other comprising foreign investors. Different macro categories of investors present a different degree of concentration of aggregated stock portfolios with highest concentration observed for foreign investors. For individual Finnish retail investors, portfolio concentration estimated by the Herfindahl-Hirschman index presents high values for more than half of the total number of retail investors. In spite of the observation that retail stock portfolios are often composed by just a few stocks, the concentration of the aggregated stock portfolio for Finnish retail investors has a portfolio concentration comparable with the one of Finnish institutional investors. Within retail investors, stock portfolios of women present a similar pattern of portfolios of men but with a systematic higher level of concentration observed for women both at individual and at aggregated level.
Online sexual predators target children by building trust, creating dependency, and arranging meetings for sexual purposes. This poses a significant challenge for online communication platforms that strive to monitor and remove such content and terminate predators' accounts. However, these platforms can only take such actions if sexual predators explicitly violate the terms of service, not during the initial stages of relationship-building. This study designed and evaluated a strategy to prevent sexual predation and victimization by delivering warnings and raising awareness among high-risk individuals based on the routine activity theory in criminal psychology. We identified high-risk users as those with a high probability of committing or being subjected to violations, using a machine learning model that analyzed social networks and monitoring data from the platform. We conducted a randomized controlled trial on a Japanese avatar-based communication application, Pigg Party. High-risk players in the intervention group received warnings and awareness-building messages, while those in the control group did not receive the messages, regardless of their risk level. The trial involved 12,842 high-risk players in the intervention group and 12,844 in the control group for 138 days. The intervention successfully reduced violations and being violated among women for 12 weeks, although the impact on men was limited. These findings contribute to efforts to combat online sexual abuse and advance understanding of criminal psychology.
Janneke Pieters, Ana Kujundzic, Rulof Burger
et al.
Technological change can have profound impacts on the labor market. Decades of research have made it clear that technological change produces winners and losers. Machines can replace some types of work that humans do, while new technologies increase human's productivity in other types of work. For a long time, highly educated workers benefitted from increased demand for their labor due to skill-biased technological change, while the losers were concentrated at the bottom of the wage distribution (Katz and Autor, 1999; Goldin and Katz, 2007, 2010; Kijima, 2006). Currently, however, labor markets seem to be affected by a different type of technological change, the so-called routine-biased technological change (RBTC). This chapter studies the risk of automation in developing country labor markets, with a particular focus on differences between men and women. Given the pervasiveness of gender occupational segregation, there may be important gender differences in the risk of automation. Understanding these differences is important to ensure progress towards equitable development and gender inclusion in the face of new technological advances. Our objective is to describe the gender gap in the routine task intensity of jobs in developing countries and to explore the role of occupational segregation and several worker characteristics in accounting for the gender gap.
Hip fractures are a major cause of disability, mortality, and healthcare burden in older adults, underscoring the need for early risk assessment. However, commonly used tools such as the DXA T-score and FRAX often lack sensitivity and miss individuals at high risk, particularly those without prior fractures or with osteopenia. To address this limitation, we propose a sequential two-stage model that integrates clinical and imaging information to improve prediction accuracy. Using data from the Osteoporotic Fractures in Men Study (MrOS), the Study of Osteoporotic Fractures (SOF), and the UK Biobank, Stage 1 (Screening) employs clinical, demographic, and functional variables to estimate baseline risk, while Stage 2 (Imaging) incorporates DXA-derived features for refinement. The model was rigorously validated through internal and external testing, showing consistent performance and adaptability across cohorts. Compared to T-score and FRAX, the two-stage framework achieved higher sensitivity and reduced missed cases, offering a cost-effective and personalized approach for early hip fracture risk assessment. Keywords: Hip Fracture, Two-Stage Model, Risk Prediction, Sensitivity, DXA, FRAX
There are not one but two dimensions of bias that can be revealed through the study of large AI models: not only bias in training data or the products of an AI, but also bias in society, such as disparity in employment or health outcomes between different demographic groups. Often training data and AI output is biased for or against certain demographics (i.e. older white people are overrepresented in image datasets), but sometimes large AI models accurately illustrate biases in the real world (i.e. young black men being disproportionately viewed as threatening). These social disparities often appear in image generation AI outputs in the form of 'marked' features, where some feature of an individual or setting is a social marker of disparity, and prompts both humans and AI systems to treat subjects that are marked in this way as exceptional and requiring special treatment. Generative AI has proven to be very sensitive to such marked features, to the extent of over-emphasising them and thus often exacerbating social biases. I briefly discuss how we can use complex prompts to image generation AI to investigate either dimension of bias, emphasising how we can probe the large language models underlying image generation AI through, for example, automated sentiment analysis of the text prompts used to generate images.
Mahfuzul Haque, Abu Saleh Musa Miah, Debashish Gupta
et al.
Heart disease is a leading cause of premature death worldwide, particularly among middle-aged and older adults, with men experiencing a higher prevalence. According to the World Health Organization (WHO), non-communicable diseases, including heart disease, account for 25\% (17.9 million) of global deaths, with over 43,204 annual fatalities in Bangladesh. However, the development of heart disease detection (HDD) systems tailored to the Bangladeshi population remains underexplored due to the lack of benchmark datasets and reliance on manual or limited-data approaches. This study addresses these challenges by introducing new, ethically sourced HDD dataset, BIG-Dataset and CD dataset which incorporates comprehensive data on symptoms, examination techniques, and risk factors. Using advanced machine learning techniques, including Logistic Regression and Random Forest, we achieved a remarkable testing accuracy of up to 96.6\% with Random Forest. The proposed AI-driven system integrates these models and datasets to provide real-time, accurate diagnostics and personalized healthcare recommendations. By leveraging structured datasets and state-of-the-art machine learning algorithms, this research offers an innovative solution for scalable and effective heart disease detection, with the potential to reduce mortality rates and improve clinical outcomes.
Firmware corpora for vulnerability research should be scientifically sound. Yet, several practical challenges complicate the creation of sound corpora: Sample acquisition, e.g., is hard and one must overcome the barrier of proprietary or encrypted data. As image contents are unknown prior analysis, it is hard to select high-quality samples that can satisfy scientific demands. Ideally, we help each other out by sharing data. But here, sharing is problematic due to copyright laws. Instead, papers must carefully document each step of corpus creation: If a step is unclear, replicability is jeopardized. This has cascading effects on result verifiability, representativeness, and, thus, soundness. Despite all challenges, how can we maintain the soundness of firmware corpora? This paper thoroughly analyzes the problem space and investigates its impact on research: We distill practical binary analysis challenges that significantly influence corpus creation. We use these insights to derive guidelines that help researchers to nurture corpus replicability and representativeness. We apply them to 44 top tier papers and systematically analyze scientific corpus creation practices. Our comprehensive analysis confirms that there is currently no common ground in related work. It shows the added value of our guidelines, as they discover methodical issues in corpus creation and unveil miniscule step stones in documentation. These blur visions on representativeness, hinder replicability, and, thus, negatively impact the soundness of otherwise excellent work. Finally, we show the feasibility of our guidelines and build a new, replicable corpus for large-scale analyses on Linux firmware: LFwC. We share rich meta data for good (and proven) replicability. We verify unpacking, deduplicate, identify contents, provide ground truth, and show LFwC's utility for research.
In recent decades, computer science (CS) has undergone remarkable growth and diversification. Creating attractive, social, or hands-on games has already been identified as a possible approach to get teenagers and young adults interested in CS. However, overcoming the global gap between the interest and participation of men and women in CS is still a worldwide problem. To address this challenge, we present a multiplayer game that is used in a workshop setting to motivate girls to program through a 3D game environment. The paper aims to expand the educational landscape within computer science education by offering a motivating and engaging platform for young women to explore programming quests in a collaborative environment. The study involved 235 girls and 50 coaches for the workshop evaluation and a subset of 20 participants for an in-game analysis. In this paper, we explore the engagement in programming and assess the cognitive workload while playing and solving programming quests within the game, as well as the learning experience and the outcome. The results show that the positive outcomes of the workshop underscore the effectiveness of a game-based collaborative learning approach to get girls interested in computer science activities. The variety of solutions found for the different tasks demonstrates the creativity and problem-solving skills of the participants and underlines the effectiveness of the workshop in promoting critical thinking and computational skills.
Sayma Sultana, Jaydeb Sarker, Farzana Israt
et al.
In an industry dominated by straight men, many developers representing other gender identities and sexual orientations often encounter hateful or discriminatory messages. Such communications pose barriers to participation for women and LGBTQ+ persons. Due to sheer volume, manual inspection of all communications for discriminatory communication is infeasible for a large-scale Free Open-Source Software (FLOSS) community. To address this challenge, this study aims to develop an automated mechanism to identify Sexual orientation and Gender identity Discriminatory (SGID) texts from software developers' communications. On this goal, we trained and evaluated SGID4SE ( Sexual orientation and Gender Identity Discriminatory text identification for (4) Software Engineering texts) as a supervised learning-based SGID detection tool. SGID4SE incorporates six preprocessing steps and ten state-of-the-art algorithms. SGID4SE implements six different strategies to improve the performance of the minority class. We empirically evaluated each strategy and identified an optimum configuration for each algorithm. In our ten-fold cross-validation-based evaluations, a BERT-based model boosts the best performance with 85.9% precision, 80.0% recall, and 82.9% F1-Score for the SGID class. This model achieves 95.7% accuracy and 80.4% Matthews Correlation Coefficient. Our dataset and tool establish a foundation for further research in this direction.
Krishna Teja Chitty-Venkata, Sparsh Mittal, Murali Emani
et al.
Recent years have seen a phenomenal rise in performance and applications of transformer neural networks. The family of transformer networks, including Bidirectional Encoder Representations from Transformer (BERT), Generative Pretrained Transformer (GPT) and Vision Transformer (ViT), have shown their effectiveness across Natural Language Processing (NLP) and Computer Vision (CV) domains. Transformer-based networks such as ChatGPT have impacted the lives of common men. However, the quest for high predictive performance has led to an exponential increase in transformers' memory and compute footprint. Researchers have proposed techniques to optimize transformer inference at all levels of abstraction. This paper presents a comprehensive survey of techniques for optimizing the inference phase of transformer networks. We survey techniques such as knowledge distillation, pruning, quantization, neural architecture search and lightweight network design at the algorithmic level. We further review hardware-level optimization techniques and the design of novel hardware accelerators for transformers. We summarize the quantitative results on the number of parameters/FLOPs and accuracy of several models/techniques to showcase the tradeoff exercised by them. We also outline future directions in this rapidly evolving field of research. We believe that this survey will educate both novice and seasoned researchers and also spark a plethora of research efforts in this field.
Gender imbalance is a well-known phenomenon observed throughout sciences which is particularly severe in software development and Free/Open Source Software communities. Little is know yet about the geography of this phenomenon in particular when considering large scales for both its time and space dimensions. We contribute to fill this gap with a longitudinal study of the population of contributors to publicly available software source code. We analyze the development history of 160 million software projects for a total of 2.2 billion commits contributed by 43 million distinct authors over a period of 50 years. We classify author names by gender using name frequencies and author geographical locations using heuristics based on email addresses and time zones. We study the evolution over time of contributions to public code by gender and by world region. For the world overall, we confirm previous findings about the low but steadily increasing ratio of contributions by female authors. When breaking down by world regions we find that the long-term growth of female participation is a worldwide phenomenon. We also observe a decrease in the ratio of female participation during the COVID-19 pandemic, suggesting that women's ability to contribute to public code has been more hindered than that of men.
Julie Fitzpatrick, Nicolas Basty, Madeleine Cule
et al.
Psoas muscle measurements are frequently used as markers of sarcopenia and predictors of health. Manually measured cross-sectional areas are most commonly used, but there is a lack of consistency regarding the position of the measurementand manual annotations are not practical for large population studies. We have developed a fully automated method to measure iliopsoas muscle volume (comprised of the psoas and iliacus muscles) using a convolutional neural network. Magnetic resonance images were obtained from the UK Biobank for 5,000 male and female participants, balanced for age, gender and BMI. Ninety manual annotations were available for model training and validation. The model showed excellent performance against out-of-sample data (dice score coefficient of 0.912 +/- 0.018). Iliopsoas muscle volumes were successfully measured in all 5,000 participants. Iliopsoas volume was greater in male compared with female subjects. There was a small but significant asymmetry between left and right iliopsoas muscle volumes. We also found that iliopsoas volume was significantly related to height, BMI and age, and that there was an acceleration in muscle volume decrease in men with age. Our method provides a robust technique for measuring iliopsoas muscle volume that can be applied to large cohorts.
For a two-sided ($n$ men/$n$ women) stable matching problem) Gale and Shapley studied a proposal algorithm (men propose/women select, or the other way around), that determines a matching, not blocked by any unmatched pair. Irving used this algorithm as a first phase of his algorithm for one-sided (stable roommates) matching problem with $n$ agents. We analyze a fully extended version of Irving's proposal algorithm that runs all the way until either each agent holds a proposal or an agent gets rejected by everybody on the agent's preference list. It is shown that the terminal, directed, partnerships form a stable permutation with matched pairs remaining matched in any other stable permutation. A likely behavior of the proposal algorithm is studied under assumption that all $n$ rankings are independently uniform. It is proved that with high probability (w.h.p.) every agent has a partner, and that both the number of agents in cycles of length $\ge 3$ and the total number of stable matchings are bounded in probability. W.h.p. the total number of proposals is asymptotic to $0.5 n^{3/2}$.
The stairwell is the main path for emergency evacuation of people in super high-rise buildings, so revealing the movement characteristics in the stairwell based on experimental data is the basis for controlling the evacuation process of super high-rise buildings to ensure the safety of the crowd. Here, an evacuation experiment is carried out in Shanghai Tower with a vertical height of 583 m, which is the second tallest building in the world. The results show that pedestrians would set a "suitable velocity" for themselves according to the target height distance, and farther distance will result in lower "suitable velocity". The evidence is that the group of participants with a 9.63% higher traveling height spend a 16.39% longer evacuation time, yet within each group the velocity do not decrease with the increase of the moving distance. Furthermore, crowding in stairwell determines whether pedestrians can achieve the "suitable velocity", and the "suitable velocity" of women or older people is smaller than men or younger people in the same scenario. The local velocity and vertical velocity of different groups, genders and ages are classified and analyzed. A new measurement method for crowd density and then the fundamental diagram of the velocity-density relation in super high-rise building is presented. These results can provide basic data for the design of emergency evacuation facilities and formulation of emergency plan for super high-rise buildings.
Knowledge production is dominated by publications in and from the global North. This has given rise to a concern that certain perspectives and agendas have global prominence whereas others, from the global South, are marginalized. Analyzing the publication record of Men and Masculinities with respect to articles authored by scholars from, or working in, South Africa, I argue that the journal, despite being founded, based and published in the United States, has a very good record of providing space for Southern gendered perspectives to emerge.
In this paper, we aim to discover archetypical patterns of individual evolution in large social networks. In our work, an archetype comprises of $\textit{progressive stages}$ of distinct behavior. We introduce a novel Gaussian Hidden Markov Model (G-HMM) Cluster to identify archetypes of evolutionary patterns. G-HMMs allow for: near limitless behavioral variation; imposing constraints on how individuals can evolve; different evolutionary rates; and are parsimonious. Our experiments with Academic and StackExchange dataset discover insightful archetypes. We identify four archetypes for researchers: $\textit{Steady}$, $\textit{Diverse, Evolving and Diffuse}$. We observe clear differences in the evolution of male and female researchers within the same archetype. Specifically, women and men differ within an archetype (e.g. Diverse) in how they start, how they transition and the time spent in mid-career. We also found that the differences in grant income are better explained by the differences in archetype than by differences in gender. For StackOverflow, discovered archetypes could be labeled as $\textit{Experts, Seekers, Enthusiasts, and Facilitators}$. We have strong quantitative results with competing baselines for activity prediction and perplexity. For future session prediction, the proposed G-HMM cluster model improves by an average of $32\%$ for different Stack Exchanges and $24\%$ for Academic dataset. Our model also exhibits lower perplexity than the baselines.
What would you do if you were invited to play a game where you were given \$25 and allowed to place bets for 30 minutes on a coin that you were told was biased to come up heads 60% of the time? This is exactly what we did, gathering 61 young, quantitatively trained men and women to play this game. The results, in a nutshell, were that the majority of these 61 players did not place their bets very well, displaying a broad panoply of behaviorial and cognitive biases. About 30% of the subjects actually went bust, losing their full \$25 stake. We also discuss optimal betting strategies, valuation of the opportunity to play the game and its similarities to investing in the stock market. The main implication of our study is that people need to be better educated and trained in how to approach decision making under uncertainty. If these quantitatively trained players, playing the simplest game we can think of involving uncertainty and favourable odds, did not play well, what hope is there for the rest of us when it comes to playing the biggest and most important game of all: investing our savings? In the words of Ed Thorp, who gave us helpful feedback on our research: "This is a great experiment for many reasons. It ought to become part of the basic education of anyone interested in finance or gambling."
A fragment of the folklore text "Apai" that was once put down on Easter Island contains a report about astronomical observations of Kirch's Comet or the Great Comet of 1680-1681 A.D. (C/1680 V1) and the partial solar eclipse of March 19, 1681 A.D. Some astronomical aspects of the local cult of bird-men have been discussed, too.