1. Mobile Cloud Computing: A Review on Smartphone Augmentation Approaches

   http://arxiv.org/abs/1205.0451v2 · arxiv

   Smartphones have recently gained significant popularity in heavy mobile processing while users are increasing their expectations toward rich computing experience. However, resource limitations and current mobile computing advancements hinder this vision. Therefore, resource-intensive application exe

2. Impact of Smartphone Distraction on Pedestrians' Crossing Behaviour: An Application of Head-Mounted Immersive Virtual Reality

   http://arxiv.org/abs/1806.06454v1 · arxiv

   A novel head-mounted virtual immersive/interactive reality environment (VIRE) is utilized to evaluate the behaviour of participants in three pedestrian road crossing conditions while 1) not distracted, 2) distracted with a smartphone, and 3) distracted with a smartphone with a virtually implemented

3. A Bleeding Digital Heart: Identifying Residual Data Generation from Smartphone Applications Interacting with Medical Devices

   http://arxiv.org/abs/1901.03724v1 · arxiv

   The integration of medical devices in everyday life prompts the idea that these devices will increasingly have evidential value in civil and criminal proceedings. However, the investigation of these devices presents new challenges for the digital forensics community. Previous research has shown that

4. Context in object detection: a systematic literature review

   http://arxiv.org/abs/2503.23249v1 · arxiv

   Context is an important factor in computer vision as it offers valuable information to clarify and analyze visual data. Utilizing the contextual information inherent in an image or a video can improve the precision and effectiveness of object detectors. For example, where recognizing an isolated obj

5. A Brief Review of Hypernetworks in Deep Learning

   http://arxiv.org/abs/2306.06955v3 · arxiv

   Hypernetworks, or hypernets for short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learning technique that allows for greater flexibility, adaptability, dynamism, faster training, information sharing, and mod

6. Smartphone Sensing for the Well-being of Young Adults: A Review

   http://arxiv.org/abs/2012.09559v4 · arxiv

   Over the years, mobile phones have become versatile devices with a multitude of capabilities due to the plethora of embedded sensors that enable them to capture rich data unobtrusively. In a world where people are more conscious regarding their health and well-being, the pervasiveness of smartphones

7. From User-independent to Personal Human Activity Recognition Models Exploiting the Sensors of a Smartphone

   http://arxiv.org/abs/1905.12285v1 · arxiv

   In this study, a novel method to obtain user-dependent human activity recognition models unobtrusively by exploiting the sensors of a smartphone is presented. The recognition consists of two models: sensor fusion-based user-independent model for data labeling and single sensor-based user-dependent m

8. FIDO2 the Rescue? Platform vs. Roaming Authentication on Smartphones

   http://arxiv.org/abs/2302.07777v1 · arxiv

   Modern smartphones support FIDO2 passwordless authentication using either external security keys or internal biometric authentication, but it is unclear whether users appreciate and accept these new forms of web authentication for their own accounts. We present the first lab study (N=87) comparing p

9. Fluctuations and Higgs mechanism in Under-Doped Cuprates: a Review

   http://arxiv.org/abs/1906.10146v1 · arxiv

   The physics of the pseudo-gap phase of high temperature cuprate superconductors has been an enduring mystery in the past thirty years. The ubiquitous presence of the pseudo-gap phase in under-doped cuprates suggests that its understanding holds a key in unraveling the origin of high temperature supe

10. YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series

    http://arxiv.org/abs/2406.19407v8 · arxiv

    This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv12. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv12 and pr

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