A majority of visually impaired population of India and other developing economies live in poverty. Accessibility without affordability has little meaning to this population. Assistive technology has great potential to make education accessible to this population, e.g. through refreshable Braille display devices. However, most existing solutions in this space remain out of reach for these users due to high cost. Innovation in data science and software engineering can play an important role in making assistive technological solutions affordable and accessible. In this paper, we present a machine-learning based automated testing approach that has played an important role in enabling us to design one of the most affordable refreshable Braille display devices of the world. The key component of our approach is a visual inspection module (VIM) created using Convolutional Neural Networks(CNNs). In our experiment, our model was able to detect malfunction of a Refreshable Braille display with 97.3% accuracy. Our model is small enough to be run on a battery-powered computer in real-time. Such accurate automatic testing methods have the potential to significantly reduce the cost of RBDs.
This real-time paper explores Insecure Direct Object Reference (IDOR) vulnerabilities in the context of bug bounty programs. It provides an overview of IDOR attacks and their impact on web application security. The paper discusses common IDOR vulnerabilities, including how they can be discovered and exploited by attackers. It also highlights the importance of implementing proper access controls and other security measures to prevent IDOR attacks. The paper concludes with a discussion of best practices for identifying and mitigating IDOR vulnerabilities in bug bounty programs, including the use of manual testing
A distinctive feature of economics that sets it apart from other fields in social sciences is that we base it on the belief that it can explain most behavioural actions by assuming that all consumers are rational and have well-defined preferences. If a decision is difficult to 'rationalise,' it is considered as an anomaly. By using various games such as “ultimatum” games and “public goods” games, the paper experimentally tries to observe the effects of bias and discrimination on the economic decisions of individuals with opponents of distinct ethnic and gender affiliation and uses the "dictator game," to detect a systematic mistrust toward players of different religious origins. By this we tried to identify whether strategies differed based on religious affiliations and gender identities. While standard economic theory claims that individuals are rational actors and will always act out of self-interest, games played with our respondents showed exciting results.
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