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Deep learning

User story-based automatic test case classification and prioritization using natural language processing-based deep learning

September 15, 2024 by Abineha Prabu, Suruthi Lavanya A, Sabarish B A iD, and Arun Kumar C

Software development starts with requirement engineering which involves establishing and documenting requirements. In agile environments, requirements are collected in the form of user stories. User stories play a crucial role in capturing functional requirements from end users. Testing and prioritizing user stories are essential to ensure whether the software meets the desired … [Read more...] about User story-based automatic test case classification and prioritization using natural language processing-based deep learning

It looks like me, but it isn’t me: On the societal implications of deepfakes

September 8, 2023 by Tamara Bonaci

Deepfakes are images or videos generated using deep learning technology to change the original conditions of a piece of media. Potential uses of this technology range from satirical content, depicting public figures in comical scenarios; to generating audio to mimic a specific voice; to inserting the face of an unknowing individual into potentially embarrassing content, for … [Read more...] about It looks like me, but it isn’t me: On the societal implications of deepfakes

Deep learning for edge devices

July 28, 2023 by Luiz Zaniolo

Deep learning (DL) has revolutionized the field of artificial intelligence (AI). At its essence, DL consists of building, training, and deploying large, multilayered neural networks. DL techniques have been successfully used in computer vision (CV), natural language processing (NLP), network security, and several other fields. As DL applications become more ubiquitous, another … [Read more...] about Deep learning for edge devices

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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.

POPULAR ARTICLE

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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