RCSB PDB Mobile: iOS and Android mobile apps to provide data access and visualization to the RCSB Protein Data Bank.

SummaryThe Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) resource provides tools for query, analysis and visualization of the 3D structures in the PDB archive. As the mobile Web is starting to surpass desktop and laptop usage, scientists and educators are beginning to integrate mobile devices into their research and teaching. In response, we have developed the RCSB PDB Mobile app for the iOS and Android mobile platforms to enable fast and convenient access to RCSB PDB data and services. Using the app, users from the general public to expert researchers can quickly search and visualize biomolecules, and add personal annotations via the RCSB PDB's integrated MyPDB service.Availability and implementationRCSB PDB Mobile is freely available from the Apple App Store and Google Play (http://www.rcsb.org)

SMILE: the creation of space for interaction through blended digital technology

Interactive Learning Environments at Sussex University is a course in which students are given mobile devices (XDAs) with PDA functionality and full Internet access for the duration of the term. They are challenged to design and evaluate learning experiences, both running and evaluating learning sessions that involve a blend of technologies. Data on technology usage was collected via backups, email and web-site logging as well as video and still photography of student-led sessions. Initial analysis indicates that large amounts of technical support, solid pedagogical underpinning and a flexible approach to both delivery context and medium are essential. The project operated under the acronym SMILE – Sussex Mobile Interactive Learning Environment

Are Drivers Who Use Cell Phones Inherently Less Safe?

Mobile phone usage while driving is increasing throughout the world. In this paper, we use survey data from 7,268 U.S. drivers to estimate the relationship between mobile phone use while driving and accidents. We hypothesize that drivers who use mobile phones while driving may be more likely to get into accidents than drivers who do not, even when they are not using the phone. We find evidence for the endogeneity of mobile phone and hands-free device usage, and our analysis suggests that individuals who are more likely to use hands-free devices are more careful drivers even without them. Once we correct for the endogeneity of usage, our models predict no statistically significant increase in accidents from mobile phone usage, whether hand-held or hands-free. Our results call into question previous cost-benefit analyses of bans on mobile phone usage while driving, which typically assume that such bans will have a salutary effect

Predicting Fraud in Mobile Phone Usage Using Artificial Neural Networks

Mobile phone usage involves the use of wireless communication devices that can be carried anywhere, as they require no physical connection to any external wires to work. However, mobile technology is not without its own problems. Fraud is prevalent in both fixed and mobile networks of all technologies. Frauds have plagued the telecommunication industries, financial institutions and other organizations for a long time. The aim of this research work and research publication is to apply 3 different neural network models (Fuzzy, Radial Basis and the Feedforward) to the prediction of fraud in real-life data of phone usage and also analyze and evaluate their performances with respect to their predicting capability. From the analysis and model predictability experiment carried out in this scientific research work, it was discovered that the fuzzy network model had the minimum error generated in its fraud predicting capability. Thus, its performance in terms of the error generated in this fraud prediction experiment showed that its NMSE (Normalized mean squared error) for the fraud predicted was 1.98264609. The mean absolute error (M AE = 15.00987244) for its fraud prediction was also the least; this showed that the fuzzy model fraud predictability was much better than the other two models

Towards an Approach to Identify and Assess the Mobile Eligibility of Business Processes

So far, the discussion about the usage of mobile devices in companies was strongly technology driven. However, current mobiledevices and related mobile networks have reached a high level of maturity. For this reason, technological issues are nolonger obstacles for using mobile devices within companies’ business processes. From our perspective, identifying businessprocess activities, which can be improved by mobile device support, is currently rather important. The degree of suitability ofbusiness processes for mobile device support is called the mobile eligibility of a business process. Aim of this paper is topresent an approach that supports the systematic analysis and assessment of the mobile eligibility of business processes, takinginto account a set of structured, adaptable criteria to deliberate between potential business values added by mobile devicesand the typical mobile device characteristics

Understanding face and eye visibility in front-facing cameras of smartphones used in the wild

Commodity mobile devices are now equipped with high-resolution front-facing cameras, allowing applications in biometrics (e.g., FaceID in the iPhone X), facial expression analysis, or gaze interaction. However, it is unknown how often users hold devices in a way that allows capturing their face or eyes, and how this impacts detection accuracy. We collected 25,726 in-the-wild photos, taken from the front-facing camera of smartphones as well as associated application usage logs. We found that the full face is visible about 29% of the time, and that in most cases the face is only partially visible. Furthermore, we identified an influence of users' current activity; for example, when watching videos, the eyes but not the entire face are visible 75% of the time in our dataset. We found that a state-of-the-art face detection algorithm performs poorly against photos taken from front-facing cameras. We discuss how these findings impact mobile applications that leverage face and eye detection, and derive practical implications to address state-of-the art's limitations