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Sociology

Ecofriendly autonomous smart walker for enhanced ambulatory assistance

May 24, 2024 by Isaac Asante, Lau Bee Theng, Mark Tee Kit Tsun, John Chua

The global demographic landscape is undergoing a significant transformation, characterized by a rising elderly population and an increasing number of people requiring rehabilitation and ambulatory assistance. This study introduces an innovative, ecofriendly smart walker designed to assist physically disadvantaged individuals in navigating their environment safely and … [Read more...] about Ecofriendly autonomous smart walker for enhanced ambulatory assistance

Robotics in solid waste handling: From science fiction to reality

May 12, 2022 by Juan Camilo David, Luis Felipe Ortiz-Clavijo, Sergio Armando Gutiérrez, and Carlos Julián Gállego

claw suspended in air

It is currently estimated that approximately 55% of the world population lives in urban centers (World Bank, 2020). This is partly caused by phenomena such as the migration of people from rural areas toward cities. A direct consequence of this situation is the increase in solid waste (SW) generation. For more about this article see link … [Read more...] about Robotics in solid waste handling: From science fiction to reality

Data communication challenges in smart cities

May 12, 2022 by Anjum Sheikh and Asha Ambhaikar

city buildings

The localization of jobs as well as educational opportunities in urban areas has resulted in the migration of people toward cities. According to the United Nations Human Settlement program, nearly two-thirds of the current population of Earth will be living in cities by 2050. With this growth, cities will have to deal with issues like efficient transportation, energy … [Read more...] about Data communication challenges in smart cities

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About the Magazine

IEEE Potentials Magazine is the publication dedicated to undergraduate and graduate students and young professionals. IEEE Potentials explores career strategies, the latest in research, and important technical developments. Through its articles, it also relates theories to practical applications, highlights technology’s global impact, and generates international forums that foster the sharing of diverse ideas about the profession.

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Privacy-preserving distributed learning: Techniques, applications, and future challenges

The rapid increase in data generated by connected devices has created a pressing need for privacy-preserving techniques in distributed learning. This article examines methods that enable collaborative machine learning (ML) while maintaining data security and user privacy. Key approaches such as federated learning (FL), differential privacy (DP), secure multiparty computation (SMPC), and homomorphic encryption (HE) are analyzed for their unique capabilities and various applications. FL facilitates model training across decentralized data sources, ensuring data remain local, while DP mitigates privacy risks by adding controlled noise. SMPC and HE support secure computations on encrypted data, maintaining confidentiality during processing. Despite their effectiveness, these techniques face challenges related to computational complexity, scalability, and regulatory compliance. The article reviews current advancements, practical implementations, and future directions, emphasizing the need for optimized, accessible solutions to enhance data security in distributed systems.

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