New Generation Indonesian Endemic Cattle Classification: MobileNetV2 and ResNet50

Cattle are an essential source of animal food globally, and each country possesses unique endemic cattle races. However, categorizing cattle, especially in countries like Indonesia with a large cattle population, presents challenges due to costs and subjectivity when using human experts. This research utilizes Computer Vision (CV) for image data classification to address this urgent need for automatic categorization. The main objective of this study is to develop a mobile-friendly model using CV techniques that can accurately detect and classify Indonesian endemic cattle races, such as Limosin, Madura, Pegon, and Simental. To achieve this, an object localization approach is employed to extract multiple features from distinct regions of each cattle image, including the head, ear, horn, and muzzle areas. The authors evaluate two CV algorithms, ResNet50 and MobileNetV2, to assess their performance in cattle race classification. The dataset used is facial photos of 147 cows. The results indicate that ResNet50 outperforms MobileNetV2, achieving a training data accuracy of 83.33% compared to MobileNetV2's 77.08%. Moreover, the validation accuracy of ResNet50 (76.92%) significantly surpasses MobileNetV2's (38.46%). The novel contribution of this research lies in developing a cost-effective and efficient solution for identifying endemic cattle breeds in Indonesia. The mobile-friendly model based on ResNet50 demonstrates superior accuracy, enabling cattle farmers and researchers to categorize cattle races with higher precision, reducing manual effort, and minimizing costs. In conclusion, this research provides a valuable advancement in automatic cattle categorization using CV techniques. By offering a practical and accurate model that considers Indonesia's specific cattle breeding conditions, this study contributes to the sustainable management and conservation of endemic cattle races while enhancing the efficiency of cattle farming practices

Parasite-stress promotes in-group assortative sociality: the cases of strong family ties and heightened religiosity

Throughout the world people differ in the magnitude with which they value strong family ties or heightened religiosity. We propose that this cross-cultural variation is a result of a contingent psychological adaptation that facilitates in-group assortative sociality in the face of high levels of parasite-stress while devaluing in-group assortative sociality in areas with low levels of parasite-stress. This is because in-group assortative sociality is more important for the avoidance of infection from novel parasites and for the management of infection in regions with high levels of parasite-stress compared with regions of low infectious disease stress. We examined this hypothesis by testing the predictions that there would be a positive association between parasite-stress and strength of family ties or religiosity. We conducted this study by comparing among nations and among states in the United States of America. We found for both the international and the interstate analyses that in-group assortative sociality was positively associated with parasite-stress. This was true when controlling for potentially confounding factors such as human freedom and economic development. The findings support the parasite-stress theory of sociality, that is, the proposal that parasite-stress is central to the evolution of social life in humans and other animals

Cracking Open the Black Box of Genetic Ancestry Testing

Stormfront, a well-known online forum for white nationalists, is a place for discussions about race, nation, and biology. We analyzed how members shared and discussed genetic ancestry tests (GATs), which revealed a complicated network of boundary maintenance, identity formation and justification, and biosociality within this online community. Using selection of seventy Stormfront threads discussing GAT results, this study employs primarily digital ethnographic methods to investigate how white nationalists navigate questions of self and community online. Using scientific concepts, genetic data, and multiple databases, white nationalists rely on the ambiguity of genetics and the black boxing of algorithms provided by testing companies to redefine white identity while also remaining committed to biologically-informed conceptions of race. This research raises important questions about the role of scientific data in racial formations

Stock Market Volatility around National Elections

This paper investigates a sample of 27 OECD countries to test whether national elections induce higher stock market volatility. It is found that the countryspecific component of index return variance can easily double during the week around an Election Day, which shows that investors are surprised by the election outcome. Several factors, such as a narrow margin of victory, lack of compulsory voting laws, change in the political orientation of the government, or the failure to form a coalition with a majority of seats in parliament significantly contribute to the magnitude of the election shock. Our findings have important implications for the optimal strategies of risk-averse stock market investors and participants of the option markets. --Political risk,National elections,Stock market volatility

Stock market volatiltity around national elections

This paper investigates a sample of 27 OECD countries to test whether national elections induce higher stock market volatility. It is found that the country-specific component of index return variance can easily double during the week around an Election Day, which shows that investors are surprised by the election outcome. Several factors, such as a narrow margin of victory, lack of compulsory voting laws, change in the political orientation of the government, or the failure to form a coalition with a majority of seats in parliament significantly contribute to the magnitude of the election shock. Our findings have important implications for the optimal strategies of risk-averse stock market investors and participants of the option markets.Political risk; National elections; Stock market volatility

Mathematical difficulties as decoupling of expectation and developmental trajectories

Recent years have seen an increase in research articles and reviews exploring mathematical difficulties (MD). Many of these articles have set out to explain the etiology of the problems, the possibility of different subtypes, and potential brain regions that underlie many of the observable behaviors. These articles are very valuable in a research field, which many have noted, falls behind that of reading and language disabilities. Here will provide a perspective on the current understanding of MD from a different angle, by outlining the school curriculum of England and the US and connecting these to the skills needed at different stages of mathematical understanding. We will extend this to explore the cognitive skills which most likely underpin these different stages and whose impairment may thus lead to mathematics difficulties at all stages of mathematics development. To conclude we will briefly explore interventions that are currently available, indicating whether these can be used to aid the different children at different stages of their mathematical development and what their current limitations may be. The principal aim of this review is to establish an explicit connection between the academic discourse, with its research base and concepts, and the developmental trajectory of abstract mathematical skills that is expected (and somewhat dictated) in formal education. This will possibly help to highlight and make sense of the gap between the complexity of the MD range in real life and the state of its academic science

AI Hyperrealism: Why AI Faces Are Perceived as More Real Than Human Ones

Recent evidence shows that AI-generated faces are now indistinguishable from human faces. However, algorithms are trained disproportionately on White faces, and thus White AI faces may appear especially realistic. In Experiment 1 (N = 124 adults), alongside our reanalysis of previously published data, we showed that White AI faces are judged as human more often than actual human faces-a phenomenon we term AI hyperrealism. Paradoxically, people who made the most errors in this task were the most confident (a Dunning-Kruger effect). In Experiment 2 (N = 610 adults), we used face-space theory and participant qualitative reports to identify key facial attributes that distinguish AI from human faces but were misinterpreted by participants, leading to AI hyperrealism. However, the attributes permitted high accuracy using machine learning. These findings illustrate how psychological theory can inform understanding of AI outputs and provide direction for debiasing AI algorithms, thereby promoting the ethical use of AI