Reducing families' recreational screen time led to children becoming substantially more physically active, trial investigators reported in JAMA Pediatrics. Eighty-nine families in Denmark ...participated in the trial, including 164 adults and 181 children with an average age of around 9 years. The children's screen time averaged 35.9 hours per week at baseline. While the control group followed a normal routine, the families randomly assigned to the intervention relinquished their smart-phones and tablets and were asked to limit recreational screen media to 3 hours or less per week for 2 weeks; they were considered adherent with up to 7 hours per week of screen time.
Warum sind Fettflecken auf Papier durchsichtig? Dieses physikalische Phänomen kann man mit Licht‐ und Farbsensoren untersuchen, die über die App Graphical Analysis von Computern, Tablets oder ...Smartphones aus ansteuerbar sind.
New technologies provide opportunities for the delivery of broad, flexible interventions with older adults. Focus groups were conducted to: (1) understand older adults' familiarity with, and barriers ...to, interacting with new technologies and tablets; and (2) utilize user-engagement in refining an intervention protocol.
Eighteen older adults (65-76 years old; 83.3% female) who were novice tablet users participated in discussions about their perceptions of and barriers to interacting with tablets. We conducted three separate focus groups and used a generic qualitative design applying thematic analysis to analyse the data. The focus groups explored attitudes toward tablets and technology in general. We also explored the perceived advantages and disadvantages of using tablets, familiarity with, and barriers to interacting with tablets. In two of the focus groups, participants had previous computing experience (e.g., desktop), while in the other, participants had no previous computing experience. None of the participants had any previous experience with tablet computers.
The themes that emerged were related to barriers (i.e., lack of instructions and guidance, lack of knowledge and confidence, health-related barriers, cost); disadvantages and concerns (i.e., too much and too complex technology, feelings of inadequacy, and comparison with younger generations, lack of social interaction and communication, negative features of tablets); advantages (i.e., positive features of tablets, accessing information, willingness to adopt technology); and skepticism about using tablets and technology in general. After brief exposure to tablets, participants emphasized the likelihood of using a tablet in the future.
Our findings suggest that most of our participants were eager to adopt new technology and willing to learn using a tablet. However, they voiced apprehension about lack of, or lack of clarity in, instructions and support. Understanding older adults' perceptions of technology is important to assist with introducing it to this population and maximize the potential of technology to facilitate independent living.
Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and ...tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013 and 2018, to identify and classify the architectures, infrastructure, and underlying algorithms for managing resources in fog/edge computing.
In recent years, mobile devices (e.g., smartphones and tablets) have met an increasing commercial success and have become a fundamental element of the everyday life for billions of people all around ...the world. Mobile devices are used not only for traditional communication activities (e.g., voice calls and messages) but also for more advanced tasks made possible by an enormous amount of multi-purpose applications (e.g., finance, gaming, and shopping). As a result, those devices generate a significant network traffic (a consistent part of the overall Internet traffic). For this reason, the research community has been investigating security and privacy issues that are related to the network traffic generated by mobile devices, which could be analyzed to obtain information useful for a variety of goals (ranging from fine-grained user profiling to device security and network optimization). In this paper, we review the works that contributed to the state of the art of network traffic analysis targeting mobile devices. In particular, we present a systematic classification of the works in the literature according to three criteria: 1) the goal of the analysis; 2) the point where the network traffic is captured; and 3) the targeted mobile platforms. In this survey, we consider points of capturing such as Wi-Fi access points, software simulation, and inside real mobile devices or emulators. For the surveyed works, we review and compare analysis techniques, validation methods, and achieved results. We also discuss possible countermeasures, challenges, and possible directions for future research on mobile traffic analysis and other emerging domains (e.g., Internet of Things). We believe our survey will be a reference work for researchers and practitioners in this research field.
EsbRootView is an event display for the detectors of ESSnuSB able to exploit natively all the nice devices that we have in hands today; desktop, laptops, but also smartphones and tablets.
Motivated by the increasing computational capacity of wireless user equipments (UEs), e.g., smart phones, tablets, or vehicles, as well as the increasing concerns about sharing private data, a new ...machine learning model has emerged, namely federated learning (FL), that allows a decoupling of data acquisition and computation at the central unit. Unlike centralized learning taking place in a data center, FL usually operates in a wireless edge network where the communication medium is resource-constrained and unreliable. Due to limited bandwidth, only a portion of UEs can be scheduled for updates at each iteration. Due to the shared nature of the wireless medium, transmissions are subjected to interference and are not guaranteed. The performance of FL system in such a setting is not well understood. In this paper, an analytical model is developed to characterize the performance of FL in wireless networks. Particularly, tractable expressions are derived for the convergence rate of FL in a wireless setting, accounting for effects from both scheduling schemes and inter-cell interference. Using the developed analysis, the effectiveness of three different scheduling policies, i.e., random scheduling (RS), round robin (RR), and proportional fair (PF), are compared in terms of FL convergence rate. It is shown that running FL with PF outperforms RS and RR if the network is operating under a high signal-to-interference-plus-noise ratio (SINR) threshold, while RR is more preferable when the SINR threshold is low. Moreover, the FL convergence rate decreases rapidly as the SINR threshold increases, thus confirming the importance of compression and quantization of the update parameters. The analysis also reveals a trade-off between the number of scheduled UEs and subchannel bandwidth under a fixed amount of available spectrum.
The Android operating system has been the most popular for smartphones and tablets since 2012. This popularity has led to a rapid raise of Android malware in recent years. The sophistication of ...Android malware obfuscation and detection avoidance methods have significantly improved, making many traditional malware detection methods obsolete. In this paper, we propose DL-Droid, a deep learning system to detect malicious Android applications through dynamic analysis using stateful input generation. Experiments performed with over 30,000 applications (benign and malware) on real devices are presented. Furthermore, experiments were also conducted to compare the detection performance and code coverage of the stateful input generation method with the commonly used stateless approach using the deep learning system. Our study reveals that DL-Droid can achieve up to 97.8% detection rate (with dynamic features only) and 99.6% detection rate (with dynamic + static features) respectively which outperforms traditional machine learning techniques. Furthermore, the results highlight the significance of enhanced input generation for dynamic analysis as DL-Droid with the state-based input generation is shown to outperform the existing state-of-the-art approaches.
Tablet computers are increasingly becoming commonplace in classrooms around the world. More than half of early childhood educators in the U.S. now have access to tablets, making it imperative to ...understand how they are using the device and what influences such use. The current study draws on survey data from 411 preschool educators serving 3- to 5-year-olds in school-based, center-based, and Head Start preschool programs to investigate how TPACK contextual factors (e.g., student background, teacher attitudes, and school support) influence teachers’ traditional and student-centered tablet computer practices. Results suggest that teacher-level factors—especially positive attitudes toward technology—are most influential. Overall, this study emphasizes the need for preschool teachers and teacher educators to understand and address the critical contextual factors of tablet computer use in preschool education. Implications for education policy include expanding traditional funding models beyond technology access to provide on-going educator support, and developing new initiatives that encourage novel professional development models based on the same learned-centered practices that teachers are encouraged to use themselves.
•Explores contextual TPACK factors and early childhood educator tablet computer use.•Teachers of low-income students use tablets more for student-centered learning.•Attitudes and confidence consistently predict teachers' tablet computer integration.•School support predicts teachers' use of tablets in student-centered practices.•Teachers use diverse apps, but primarily literacy, STEM, and general education apps.
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