Conversational agents could provide timely and cost-effective social support to promote behavioral changes and improve healthcare outcomes. The authors evaluated the performance of their social media-based conversational agent in a smoking cessation program. Results showed that the presence of a conversational agent effectively increased participant engagement and enhanced their smoking cessation outcomes.

Tight collaboration between manycore system designers and machine-learning experts is necessary to create a data-driven manycore design framework that integrates both learning and expert knowledge. Such a framework will be necessary to address the rising complexity of designing large-scale manycore systems and machine-learning techniques.

Smart city–based IoT devices enable collection of vast amounts of data, which can be used to provide more efficient public and private services. Among these, healthcare is especially relevant, and smart health (s-health) models are already being deployed. The authors propose attribute-based credentials (ABCs) to cope with s-health privacy issues and to set the stage for the further adoption in other privacy-aware IoT-based smart cities' services.

The IEEE Standards Association's Industry Connection program, with its International Roadmap for Devices and Systems (IRDS), takes over the Semiconductor Industry Association's International Technology Roadmap for Semiconductors (ITRS).

Akin to human transfer of experiences, transfer learning as a subfield of machine learning adapts knowledge acquired in one domain to a new domain. The authors systematically investigate how this concept might be applied to the study of users on emerging Web platforms, proposing a transfer learning–based approach, TraNet.

Jointly launched in mid-2011 by major search engines like Google and Bing, Schema.org is designed to facilitate structured and knowledge graph–centric search applications on the Web. The Web Data Commons project has crawled increasing amounts of Schema.org The authors present empirical studies of organizations in three economic sectors that expose semantically linked Schema.org

Capsule endoscopy identifies damaged areas in a patient's small intestine but often outputs poor-quality images or misses lesions, leading to either misdiagnosis or repetition of the lengthy procedure. The authors propose applying deep-learning models to automatically process the captured images and identify lesions in real time, enabling the capsule to take additional images of a specific location, adjust its focus level, or improve image quality. The authors also describe the technical challenges in realizing a viable automated capsule-endoscopy system. J. Ahn, H. Nguyen Loc, R. Krishna Balan, Y. Lee and J. Ko, "Finding Small-Bowel Lesions: Challenges in Endoscopy-Image-Based Learning Systems," in Computer, vol.

Recurrent neural networks (RNNs) have shown promising results in audio and speech-processing applications. The increasing popularity of Internet of Things (IoT) devices makes a strong case for implementing RNN-based inferences for applications such as acoustics-based authentication and voice commands for smart homes. However, the feasibility and performance of these inferences on resource-constrained devices remain largely unexplored. The authors compare traditional machine-learning models with deep-learning RNN models for an end-to-end authentication system based on breathing acoustics.

By leveraging advances in deep learning, challenging pattern recognition problems have been solved in computer vision, speech recognition, natural language processing, and more. Mobile computing has also adopted these powerful modeling approaches, delivering astonishing success in the field's core application domains, including the ongoing transformation of human activity recognition technology through machine learning.

Although the ability to collect, collate, and analyze the vast amount of data generated from cyber-physical systems and Internet of Things devices can be beneficial to both users and industry, this process has led to a number of challenges, including privacy and scalability issues. The authors present a hybrid framework where user-centered edge devices and resources can complement the cloud for providing privacy-aware, accurate, and efficient analytics.