Lifeboat Foundation

This paper describes the design, implementation, and evaluation of VanarSena, an automated fault finder for mobile applications (“apps’‘). The techniques in VanarSena are driven by a study of 25 million real-world crash reports of Windows Phone apps reported in 2012. Our analysis indicates that a modest number of root causes are responsible for many observed failures, but that they occur in a wide range of places in an app, requiring a wide coverage of possible execution paths. VanarSena adopts a “greybox’’ testing method, instrumenting the app binary to achieve both coverage and speed. VanarSena runs on cloud servers: the developer uploads the app binary; VanarSena then runs several app “monkeys’’ in parallel to emulate user, network, and sensor data behavior, returning a detailed report of crashes and failures. We have tested VanarSena with 3000 apps from the Windows Phone store, finding that 1108 of them had failures; VanarSena uncovered 2969 distinct bugs in existing apps, including 1227 that were not previously reported. Because we anticipate VanarSena being used in regular regression tests, testing speed is important. VanarSena uses two techniques to improve speed. First, it uses a “hit testing’’ method to quickly emulate an app by identifying which user interface controls map to the same execution handlers in the code. Second, it generates a ProcessingCompleted event to accurately determine when to start the next interaction. These features are key benefits of VanarSena’s greybox philosophy.

2014-06

http://hdl.handle.net/1721.1/110759

Roboticists at the University of California San Diego have developed flexible feet that can help robots walk up to 40 percent faster on uneven terrain such as pebbles and wood chips. The work has applications for search-and-rescue missions as well as space exploration.

“Robots need to be able to walk fast and efficiently on natural, uneven terrain so they can go everywhere humans can go, but maybe shouldn’t,” said Emily Lathrop, the paper’s first author and a Ph.D. student at the Jacobs School of Engineering at UC San Diego.

The researchers will present their findings at the RoboSoft conference which takes place virtually May 15 to July 15, 2020.

Is it possible some instances of artificial intelligence are not as intelligent as we thought?

Call it artificial artificial intelligence.

A team of computer graduate students reports that a closer examination of several dozen information retrieval algorithms hailed as milestones in artificial research were in fact nowhere near as revolutionary as claimed. In fact, AI used in those algorithms were often merely minor tweaks of previously established routines.

Washington State University (WSU) and Pacific Northwest National Laboratory (PNNL) researchers have created a sodium-ion battery that holds as much energy and works as well as some commercial lithium-ion battery chemistries, making for a potentially viable battery technology out of abundant and cheap materials.

The team reports one of the best results to date for a sodium-ion battery. It is able to deliver a capacity similar to some lithium-ion batteries and to recharge successfully, keeping more than 80 percent of its charge after 1,000 cycles. The research, led by Yuehe Lin, professor in WSU’s School of Mechanical and Materials Engineering, and Xiaolin Li, a senior research scientist at PNNL is published in the journal, ACS Energy Letters.

“This is a major development for sodium-ion batteries,” said Dr. Imre Gyuk, director of Energy Storage for the Department of Energy’s Office of Electricity who supported this work at PNNL. “There is great interest around the potential for replacing Li-ion batteries with Na-ion in many applications.”

Inspired by the Japanese art of paper cutting, MIT engineers have designed a friction-boosting material that could be used to coat the bottom of your shoes, giving them a stronger grip on ice and other slippery surfaces.

The researchers drew on kirigami, a variation of origami that involves cutting paper as well as folding it, to create the new coating. Laboratory tests showed that when people wearing kirigami-coated shoes walked on an icy surface, they generated more friction than the uncoated shoes.

Incorporating this coating into shoes could help prevent dangerous falls on ice and other hazardous surfaces, especially among the elderly, the researchers say.

EPFL researchers have developed electronic fibers that, when embedded in textiles, can be used to collect data about our bodies by measuring fabric deformation. Their technology employs flexible transmission lines and offers a host of applications, including in the medical industry.

Professor Fabien Sorin and doctoral assistant Andreas Leber, at the Laboratory of Photonic Materials and Fibre Devices (FIMAP) in EPFL’s School of Engineering, have developed a technology that can be used to detect a body’s movements—and a whole lot more.

“Imagine clothing or hospital bed sheets capable of monitoring your breathing and physical gestures, or AI-powered textiles that allow humans to interact more safely and intuitively with robots” says Leber. “The flexible transmission lines that we’ve developed can do all of this.”

As artificial intelligence (AI) becomes increasingly used for critical applications such as diagnosing and treating diseases, predictions and results regarding medical care that practitioners and patients can trust will require more reliable deep learning models.

In a recent preprint (available through Cornell University’s open access website arXiv), a team led by a Lawrence Livermore National Laboratory (LLNL) computer scientist proposes a novel deep learning approach aimed at improving the reliability of classifier models designed for predicting disease types from diagnostic images, with an additional goal of enabling interpretability by a medical expert without sacrificing accuracy. The approach uses a concept called confidence calibration, which systematically adjusts the model’s predictions to match the human expert’s expectations in the real world.

“Reliability is an important yardstick as AI becomes more commonly used in high-risk applications, where there are real adverse consequences when something goes wrong,” explained lead author and LLNL computational scientist Jay Thiagarajan. “You need a systematic indication of how reliable the model can be in the real setting it will be applied in. If something as simple as changing the diversity of the population can break your system, you need to know that, rather than deploy it and then find out.”

A picture may be worth a thousand words, but apparently one image is worth potentially thousands of headaches for Android users recently.

The noted tech information leaker Ice Universe this weekend posted a warning about an image that if set as wallpaper will soft-brick Samsung and Google Pixel phones. Soft-bricking triggers Android devices to continuously loop an action or freeze the unit. This generally requires a factory reset.

The image, a seemingly innocuous sunset (or dawn) sky above placid waters, may be viewed without harm. But if loaded as wallpaper, the phone will crash.

More than three-quarters of the baryonic content of the Universe resides in a highly diffuse state that is difficult to detect, with only a small fraction directly observed in galaxies and galaxy clusters^1,2. Censuses of the nearby Universe have used absorption line spectroscopy^3,4 to observe the ‘invisible’ baryons, but these measurements rely on large and uncertain corrections and are insensitive to most of the Universe’s volume and probably most of its mass. In particular, quasar spectroscopy is sensitive either to the very small amounts of hydrogen that exist in the atomic state, or to highly ionized and enriched gas^4,5,6 in denser regions near galaxies⁷. Other techniques to observe these invisible baryons also have limitations; Sunyaev–Zel’dovich analyses^8,9 can provide evidence from gas within filamentary structures, and studies of X-ray emission are most sensitive to gas near galaxy clusters^9,10. Here we report a measurement of the baryon content of the Universe using the dispersion of a sample of localized fast radio bursts; this technique determines the electron column density along each line of sight and accounts for every ionized baryon^11,12,13. We augment the sample of reported arcsecond-localized^{14,15,16,17,18} fast radio bursts with four new localizations in host galaxies that have measured redshifts of 0.291, 0.118, 0.378 and 0.522. This completes a sample sufficiently large to account for dispersion variations along the lines of sight and in the host-galaxy environments¹¹, and we derive a cosmic baryon density of \({\varOmega }_{{\rm{b}}}={0.051}_{-0.025}^{+0.021}{h}_{70}^{-1}\) (95 per cent confidence; h₇₀ = H₀/(70 km s⁻¹ Mpc⁻¹) and H₀ is Hubble’s constant). This independent measurement is consistent with values derived from the cosmic microwave background and from Big Bang nucleosynthesis^19,20.