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Predicting the Lifespan of an App

A new model predicts the two-year lifespan of an app with 85% efficiency

2 min read
Illustration of a scientist with machines hooked up to a phone, examining apps
Illustration: Shutterstock

The number of apps smartphone users have to choose from is daunting, with roughly 2 million available through the Apple Store alone. But survival of the fittest applies to the digital world too, and not all of these apps will go on to become the next Tik Tok. In a study published 29 July in IEEE Transactions on Mobile Computing, researchers describe a new model for predicting the long-term survival of apps, which outperforms seven existing designs.

“For app developers, understanding and tracking the popularity of an app is helpful for them to act in advance to prevent or alleviate the potential risks caused by the dying apps,” says Bin Guo, a professor at Northwestern Polytechnical University who helped develop the new model.

“Furthermore, the prediction of app life cycle is crucial for the decision-making of investors. It helps evaluate and assess whether the app is promising for the investors with remarkable rewards, and provides in advance warning to avoid investment failures.”

In developing their new model, AppLife, Guo’s team took a Multi-Task Learning (MTL) approach. This involves dividing data on apps into segments based on time, and analyzing factors – such as download history, ratings, and reviews – at each time interval. AppLife then predicts the likelihood of an app being removed within the next one or two years.

The researchers evaluated AppLife using a real-world dataset with more than 35,000 apps from the Apple Store that were available in 2016, but had been released the previous year. “Experiments show that our approach outperforms seven state-of-the-art methods in app survival prediction. Moreover, the precision and the recall reach up to 84.7% and 95.1%, respectively,” says Guo.

Intriguingly, AppLife was particularly good at predicting the survival of apps for tools—even more so than apps for news and video. Guo says this could be because more apps for tools exist in the dataset, feeding the model with more data to improve its performance in this respect. Or, he says, it could be caused by greater competition among tool apps, which in turn leads to more detailed and consistent user feedback.

Moving forward, Guo says he plans on building upon this work. While AppLife currently looks at factors related to individual apps, Guo is interested in exploring interactions among apps, for example which ones complement each other. Analyzing the usage logs of apps is another area of interest, he says.

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Metamaterials Could Solve One of 6G’s Big Problems

There’s plenty of bandwidth available if we use reconfigurable intelligent surfaces

12 min read
An illustration depicting cellphone users at street level in a city, with wireless signals reaching them via reflecting surfaces.

Ground level in a typical urban canyon, shielded by tall buildings, will be inaccessible to some 6G frequencies. Deft placement of reconfigurable intelligent surfaces [yellow] will enable the signals to pervade these areas.

Chris Philpot

For all the tumultuous revolution in wireless technology over the past several decades, there have been a couple of constants. One is the overcrowding of radio bands, and the other is the move to escape that congestion by exploiting higher and higher frequencies. And today, as engineers roll out 5G and plan for 6G wireless, they find themselves at a crossroads: After years of designing superefficient transmitters and receivers, and of compensating for the signal losses at the end points of a radio channel, they’re beginning to realize that they are approaching the practical limits of transmitter and receiver efficiency. From now on, to get high performance as we go to higher frequencies, we will need to engineer the wireless channel itself. But how can we possibly engineer and control a wireless environment, which is determined by a host of factors, many of them random and therefore unpredictable?

Perhaps the most promising solution, right now, is to use reconfigurable intelligent surfaces. These are planar structures typically ranging in size from about 100 square centimeters to about 5 square meters or more, depending on the frequency and other factors. These surfaces use advanced substances called metamaterials to reflect and refract electromagnetic waves. Thin two-dimensional metamaterials, known as metasurfaces, can be designed to sense the local electromagnetic environment and tune the wave’s key properties, such as its amplitude, phase, and polarization, as the wave is reflected or refracted by the surface. So as the waves fall on such a surface, it can alter the incident waves’ direction so as to strengthen the channel. In fact, these metasurfaces can be programmed to make these changes dynamically, reconfiguring the signal in real time in response to changes in the wireless channel. Think of reconfigurable intelligent surfaces as the next evolution of the repeater concept.

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