Reducing distraction of smartwatch users with deep learning

Lee, Jemin; Kwon, Jinse; Kim, Hyungshin

doi:10.1145/2957265.2962662

Cited by 11 publications

(7 citation statements)

References 13 publications

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“…Another interesting application is presented in [284], where Lee et al show that deep learning can help smartwatch users reduce distraction by eliminating unnecessary notifications. Specifically, the authors use an 11-layer MLP to predict the importance of a notification.…”

Section: Deep Learning Driven App-level Mobile Data Analysismentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,377

838

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The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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Section: Deep Learning Driven App-level Mobile Data Analysismentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,377

838

View full text Add to dashboard Cite

show abstract

“…Lee et al. demonstrated that ML could limit alert messages in smartwatches [168]. From these reviews, it is clear that app data are complex and heterogeneous, and therefore special care must be taken to deploy this processing at the low‐powered terminals and mobile devices.…”

Section: Intelligent Next‐generation Wireless Networkmentioning

confidence: 99%

Scope of machine learning applications for addressing the challenges in next‐generation wireless networks

Samanta¹,

Sadhukhan²,

Samaddar³

et al. 2022

CAAI Trans on Intel Tech

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The convenience of availing quality services at affordable costs anytime and anywhere makes mobile technology very popular among users. Due to this popularity, there has been a huge rise in mobile data volume, applications, types of services, and number of customers. Furthermore, due to the COVID‐19 pandemic, the worldwide lockdown has added fuel to this increase as most of our professional and commercial activities are being done online from home. This massive increase in demand for multi‐class services has posed numerous challenges to wireless network frameworks. The services offered through wireless networks are required to support this huge volume of data and multiple types of traffic, such as real‐time live streaming of videos, audios, text, images etc., at a very high bit rate with a negligible delay in transmission and permissible vehicular speed of the customers. Next‐generation wireless networks (NGWNs, i.e. 5G networks and beyond) are being developed to accommodate the service qualities mentioned above and many more. However, achieving all the desired service qualities to be incorporated into the design of the 5G network infrastructure imposes large challenges for designers and engineers. It requires the analysis of a huge volume of network data (structured and unstructured) received or collected from heterogeneous devices, applications, services, and customers and the effective and dynamic management of network parameters based on this analysis in real time. In the ever‐increasing network heterogeneity and complexity, machine learning (ML) techniques may become an efficient tool for effectively managing these issues. In recent days, the progress of artificial intelligence and ML techniques has grown interest in their application in the networking domain. This study discusses current wireless network research, brief discussions on ML methods that can be effectively applied to the wireless networking domain, some tools available to support and customise efficient mobile system design, and some unresolved issues for future research directions.

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“…In addition, smartphone push notifications provide updates on our social environment which would be necessary for our group's survival; however, too many notifications pushed our way can become distractions from group survival, so the balance between a constant demand to orient towards a notification versus ignoring all notifications requires choices by the users of smartphone technology (Lee, Kwon, & Kim, 2016).…”

Section: Push Notificationsmentioning

confidence: 99%

Digital Addiction: Increased Loneliness, Anxiety, and Depression

Peper

Harvey

2018

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Digital addiction is defined by the American Society for Addiction Medicine (ASAM) as well as the American Psychiatric Association (APA) as "… a primary, chronic disease of brain reward, motivation, memory, and related circuitry. Dysfunction in these circuits leads to characteristic biological, psychological, social, and spiritual manifestations. This is reflected in an individual pathologically pursuing reward and/or relief by substance use and other behaviors…" with examples such a such as internet gaming or similar behaviors. Symptoms of digital addiction such as increased loneliness (also called "phoneliness"), anxiety, and depression were observed in a sample of university undergraduates who completed a survey about smartphone use during and outside of class. Other observations included observations of "iNeck" (poor) posture as well as how multitasking/semitasking was prevalent in the sample. Implications of continued digital addition are discussed.

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Reducing distraction of smartwatch users with deep learning

Cited by 11 publications

References 13 publications

Deep Learning in Mobile and Wireless Networking: A Survey

Deep Learning in Mobile and Wireless Networking: A Survey

Scope of machine learning applications for addressing the challenges in next‐generation wireless networks

Digital Addiction: Increased Loneliness, Anxiety, and Depression

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