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Abstract: Counterfeit money has always existed an issue that has caused many problems in the market. Technological growth development has made it possible to create extra counterfeit items which are distributed in the mitigation market the global economy. Bangui existing banking equipment and so on trading sites to check the authenticity of funds. But the average person does not do that have access to such systems and that is why they are needed in order for the software to receive counterfeit money, which can be used by ordinary people. This the proposed system uses image processing to find out if the money is real or fake. System built uses the Python system completely language. It contains similar steps grayscale modification, edge detection, separation, etc. made using appropriate methods. Keyword: Counterfeit currency, Image Processing, Python programming language, grayscale conversion, edge detection, segmentation.

Throughout the modern age, sports have been a very important part of human existence. As our documentation of sports has become more advanced, so have the prediction capabilities. Presently, analysts keep track of a massive amount of information about each team, player, coach, and matchup. This collection has led to the development of unparalleled prediction systems with high levels of accuracy. The issue with these prediction systems is that they are proprietary and very costly to maintain. In other words, they are unusable by the average person. Sports, being one of the most heavily analyzed activities on the planet, should be accessible to everyone. In this paper, a preliminary system for using publicly available statistics and open-source methods for predicting NBA All-Stars is introduced and modified to improve the accuracy of the predictions, which reaches values close to 0.9 in raw accuracy, and higher than 0.9 in specificity.

Black Americans who are perceived as more racially phenotypical—that is, who possess more physical traits that are closely associated with their race—are more often associated with racial stereotypes. These stereotypes, including assumptions about criminality, can influence how Black Americans are treated by the legal system. However, it is unclear whether other forms of racial stereotypicality, such as a person’s way of speaking, also activate stereotypes about Black Americans. We investigated the links between speech stereotypicality and racial stereotypes (Experiment 1) and racial phenotype bias (Experiment 2). In Experiment 1, participants listened to audio recordings of Black speakers and rated how stereotypical they found the speaker, the likely race and nationality of the speaker, and indicated which adjectives the average person would likely associate with this speaker. In Experiment 2, participants listened to recordings of weakly or strongly stereotypical Black American speakers and indicated which of two faces (either weakly or strongly phenotypical) was more likely to be the speaker’s. We found that speakers whose voices were rated as more highly stereotypical for Black Americans were more likely to be associated with stereotypes about Black Americans (Experiment 1) and with more stereotypically Black faces (Experiment 2). These findings indicate that speech stereotypicality activates racial stereotypes as well as expectations about the stereotypicality of an individual’s appearance. As a result, the activation of stereotypes based on speech may lead to bias in suspect descriptions or eyewitness identifications.

The prevalence of mental diseases is increasing. Even the average person suffers from minor issues such as stress and anxiety due to various causes. Clients having psychiatric consultations require support and exceptional care from a psychiatric nurse; these clients are given multiple drugs with varied side effects that must be managed; the nurse is only present with the patient and manages the patient. As a result, this article discusses the numerous roles that a psychiatric nurse can play in this regard.

The better-than-average effect refers to the tendency to rate oneself as better than the average person on desirable traits and skills. In a classic study, Svenson (1981) asked participants to rate their driving safety and skill compared to other participants in the experiment. Results showed that the majority of participants rated themselves as far above the median, despite the statistical impossibility of more than 50% of participants being above the median. We report a preregistered, well-powered (total N = 1,203), very close replication and extension of the Svenson (1981) study. Our results indicate that the majority of participants rated their driving skill and safety as above average. We added different response scales as an extension and findings were stable across all three mesaures. Thus, our findings are consistent with the original findings by Svenson (1981). Materials, data, and code are available at https://osf.io/fxpwb/

Since the global onset of COVID-19 in early 2020, the disease has significantly impacted mental health. This impact is likely to be further exacerbated for groups who were already marginalized. This paper shares results from a broader study of men who have sex with men (MSM) and transgender people in Bali, Indonesia and includes a focus on psychological distress and happiness during the COVID-19 pandemic; applying sociodemographic and epidemiological characteristics as potential mediators. Psychological distress and the level of happiness were measured by The Kessler Psychological Distress (K10) and the Subjective Happiness Scale (SHS). A cross-sectional survey was conducted from July to September 2020. Of the 416 participants, complete data were available for 363 participants. The majority of participants were aged 26–40 years, currently single, were born outside Bali, were currently living in an urban area, and over one-third were living with HIV. While all were MSM, the majority identified as homosexual/tend to be homosexual (71.3%), however 54 (14.9%) identified themselves as heterosexual. The majority (251, 69.1%) reported moderate to very high psychological distress during the COVID-19 pandemic. The binary logistic regression analysis identified five factors to be significantly associated with higher psychological distress: being a student, reporting higher levels of stigma, had ever experienced discrimination, felt better prior to the COVID-19 pandemic, and less happy than the average person. When homosexual were compared with heterosexual participants, those who identified themselves as being homosexual reported significantly lower psychological distress compared to those identified themselves as heterosexual, which may be associated with these participants not disclosing their status as MSM and the stigma around MSM. Those who considered themselves to be less happy than the average person (316, 87.1%) were more likely to live with a partner and to report moderate to very high psychological distress. Based on the findings, interventions should focus on strategies to reduce stigma, provide non-discriminatory services, and improve access to essential health services.

Person judgments reflect perceiver effects: differences in how perceivers judge the average person. The factorial structure of such effects is still discussed. We present a large-scale, preregistered replication study using over 1 million person judgments (different groups of 200 perceivers judged 200 targets in one of 20 situations, using 30 personality items). Results unanimously favored a model comprising three systematic components: acquiescence (endorsing all items more than other perceivers), positivity (endorsing positive over negative items), and trait specificity (endorsing items reflecting a specific trait more). The latter two factors each accounted for approximately a quarter of the variance in perceiver effects, and acquiescence accounted for less than 10%. Positivity was more influential for evaluative items and was strongly associated with how likable perceivers found their targets to be ( r = .55). With considerable statistical power and generalizability, our findings significantly improve the knowledge base regarding the structure of perceiver effects.

Industry 4.0 provides opportunities and threats to life, such as income inequality, nature destruction, cybercrime, and internet dependence. Threats also occur in the education field. In 2019, a social media platform called ‘we are social’ published a study revealing that the average person spends six hours a day using the internet. Four out of ten ASEAN member countries are included in the top ten social media user countries. Indonesia is one of the countries belonging to this category. Similarly, Singapore also has a relatively high level of social media use. However, its conditions tend to be stable, making it easier for Singapore to overcome those problems. On the other hand, with a larger population, it is more difficult to control such problems in Indonesia. ASEAN, an international organization, strives to reduce disparities between Singapore and Indonesia through joint integration between its member countries. In this paper, the author explains about ASEAN businesses in facing disparities in the region, especially concerning industrial readiness 4.0. The author utilized the theory of international organizations and the human development index concept to analyze the case study.

Abstract Moral expertise requires a level of reliability in moral matters that is significantly higher than that of the average person. The author argues that this requirement of epistemic superiority in moral matters is sometimes fulfilled by our future selves and generally fulfilled by present moral philosophers. Our future selves are more reliable in answering moral questions than we are, when they have been prepared to answer those questions by various epistemic activities. But if our future selves are our epistemic superiors in such cases, moral philosophers are epistemic superiors in moral matters more generally since their epistemic advantages are even more significant. The author concludes by arguing that moral philosophers plausibly even qualify as moral experts.

Flutter SDK developed by Google Inc. provides fast and easy way to develop application targeting multiple platforms using single code base, without compromising on features and performance. Application developed using this framework are on par with native apps developed for the platform, resulting in great performance and efficient resource utilization. The application proposed here will be able to assist visually impaired people to manage their email-box without any assistance of other people and they can even compose emails, send replies to emails received and listen to received emails using text to voice and voice to text techniques proposed. For an average person it may seem a simple task to manage their own email-box but to a person who is not able to see, this task seems near impossible without assistance from other people. The proposed application helps visually impaired peoples to easily tackle these challenges and ensures private and secure email-box management.