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Register a new userMultidevice mobile sessions: A first look
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Added by
Benjamin Finley
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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The increasing number of users with multiple mobile devices underscores the importance of understanding how users interact, often simultaneously, with these multiple devices. However, most device based monitoring studies have focused only on a single device type. In contrast, we study the multidevice usage of a US-based panel through device based monitoring on panelists smartphone and tablet devices. We present a broad range of results from characterizing individual multidevice sessions to estimating device usage substitution. For example, we find that for panelists, 50% of all device interaction time can be considered multidevice usage.
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As the COVID-19 pandemic emerged in early 2020, a number of malicious actors have started capitalizing the topic. Although a few media reports mentioned the existence of coronavirus-themed mobile malware, the research community lacks the understanding of the landscape of the coronavirus-themed mobile malware. In this paper, we present the first systematic study of coronavirus-themed Android malware. We first make efforts to create a daily growing COVID-19 themed mobile app dataset, which contains 4,322 COVID-19 themed apk samples (2,500 unique apps) and 611 potential malware samples (370 unique malicious apps) by the time of mid-November, 2020. We then present an analysis of them from multiple perspectives including trends and statistics, installation methods, malicious behaviors and malicious actors behind them. We observe that the COVID-19 themed apps as well as malicious ones began to flourish almost as soon as the pandemic broke out worldwide. Most malicious apps are camouflaged as benign apps using the same app identifiers (e.g., app name, package name and app icon). Their main purposes are either stealing users private information or making profit by using tricks like phishing and extortion. Furthermore, only a quarter of the COVID-19 malware creators are habitual developers who have been active for a long time, while 75% of them are newcomers in this pandemic. The malicious developers are mainly located in US, mostly targeting countries including English-speaking countries, China, Arabic countries and Europe. To facilitate future research, we have publicly released all the well-labelled COVID-19 themed apps (and malware) to the research community. Till now, over 30 research institutes around the world have requested our dataset for COVID-19 themed research.
The number of visually impaired or blind (VIB) people in the world is estimated at several hundred million. Based on a series of interviews with the VIB and developers of assistive technology, this paper provides a survey of machine-learning based mobile applications and identifies the most relevant applications. We discuss the functionality of these apps, how they align with the needs and requirements of the VIB users, and how they can be improved with techniques such as federated learning and model compression. As a result of this study we identify promising future directions of research in mobile perception, micro-navigation, and content-summarization.
Combining low cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. We present a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system - Smartphone Brain Scanner - combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully mobile system for real-time 3D EEG imaging. We discuss the benefits and challenges of a fully portable system, including technical limitations as well as real-time reconstruction of 3D images of brain activity. We present examples of the brain activity captured in a simple experiment involving imagined finger tapping, showing that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using a off-the-shelf consumer neuroheadset is lower compared to that obtained using high density standard EEG equipment, we propose that mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings.
Recently emerging Decentralized Finance (DeFi) takes the promise of cryptocurrencies a step further, leveraging their decentralized networks to transform traditional financial products into trustless and transparent protocols that run without intermediaries. However, these protocols often require critical external information, like currency or commodity exchange rates, and in this respect they rely on special oracle nodes. In this paper, we present the first study of DeFi oracles deployed in practice. First, we investigate designs of mainstream DeFi platforms that rely on data from oracles. We find that these designs, surprisingly, position oracles as trusted parties with no or low accountability. Then, we present results of large-scale measurements of deployed oracles. We find and report that prices reported by oracles regularly deviate from current exchange rates, oracles are not free from operational issues, and their reports include anomalies. Finally, we compare the oracle designs and propose potential improvements.
Mobile device users avoiding observational attacks and coping with situational impairments may employ techniques for eyes-free mobile unlock authentication, where a user enters his/her passcode without looking at the device. This study supplies an initial description of user accu- racy in performing this authentication behavior with PIN and pattern passcodes, with varying lengths and visual characteristics. Additionally, we inquire if tactile-only feedback can provide assistive spatialization, finding that orientation cues prior to unlocking do not help. Measure- ments of edit distance and dynamic time warping accuracy were collected, using a within-group, randomized study of 26 participants. 1,021 passcode entry gestures were collected and classified, identifying six user strategies for using the pre-entry tactile feedback, and ten codes for types of events and errors that occurred during entry. We found that users who focused on orienting themselves to position the first digit of the passcode using the tactile feedback performed better in the task. These results could be applied to better define eyes-free behavior in further research, and to design better and more secure methods for eyes-free authentication.
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