The BadBox Android malware botnet has grown to over 192,000 infected devices worldwide despite a recent sinkhole operation that attempted to disrupt the operation in Germany.
Researchers from BitSight warn that the malware appears to have expanded its targeting scope beyond no-name Chinese Android devices, now infecting more well-known and trusted brands like Yandex TVs and Hisense smartphones.
A new technology that can generate electricity from vibrations or even small body movements means you could charge your laptop by typing or power your smartphone’s battery on your morning run.
Researchers at the University of Waterloo have developed a tiny, wearable generator in response to the urgent need for sustainable, clean energy. It is also scalable for larger machines. Their paper, “Breaking Dielectric Dilemma: Polymer Functionalized Perovskite Piezocomposite with Large Current Density Output,” is published in the November edition of Nature Communications.
“This is a real game changer,” said Dr. Asif Khan, the project’s lead researcher and a postdoctoral fellow in the Department of Electrical and Computer Engineering at Waterloo. “We have made the first device of its kind that can power electronics at low cost and with unprecedented efficiency.”
A small team of AI researchers at Adobe Inc., working with a colleague from Auburn University and another from Georgia Tech, has developed a small language model (SLM) that they claim can be run locally on a smart phone with no access to the cloud. The group has written a paper describing their new app, which they call SlimLM, and have posted it to the arXiv preprint server.
As LLM technology continues to mature, researchers across the globe continue to find new ways to improve it. In this new effort, the research team has found a way to cut the cord for a specific type of AI application—processing documents locally.
As LLMs such as ChatGPT become more popular, users have become more worried about privacy. And it is not just individuals—companies large and small have adopted AI applications that assist with a variety of business processes, some of which require a high degree of privacy.
The Spanish police, working with colleagues in Peru, conducted a simultaneous crackdown on a large-scale voice phishing (vishing) scam ring in the two countries, arresting 83 individuals.
Thirty-five of the arrested people were located across Spain, including in Madrid, Barcelona, Mallorca, Salamanca, and Vigo, and another 48 were arrested in Peru.
The leader of the ring was also apprehended in Spain during the 29 simultaneous raids conducted by the cooperating police forces, which also seized cash, mobile phones, computers, and documents.
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Exploiting the promise of recent advances in imitation learning for mobile manipulation will require the collection of large numbers of human-guided demonstrations. This paper proposes an open-source design for an inexpensive, robust, and flexible mobile manipulator that can support arbitrary arms, enabling a wide range of real-world household mobile manipulation tasks. Crucially, our design uses powered casters to enable the mobile base to be fully holonomic, able to control all planar degrees of freedom independently and simultaneously. This feature makes the base more maneuverable and simplifies many mobile manipulation tasks, eliminating the kinematic constraints that create complex and time-consuming motions in nonholonomic bases. We equip our robot with an intuitive mobile phone teleoperation interface to enable easy data acquisition for imitation learning.
For those unaware, Whisk3D (original name Blendersito) is Dante’s Symbian-powered version of Blender, which he has been developing since late 2022. The app allows users to upload and model 3D characters on the phone, design game level assets, extrude vertices and edges, create planes, and even connect the phone to a monitor and keyboard for more convenient use.
In a true Blender fashion, Dante’s Whisk3D is open-source and can be accessed via the creator’s GitHub page. You can also support Dante here and check out more jaw-dropping experiments with Nokia-ran Blender over here.
Two years ago, a medical professional approached scientists at the University of Tabriz in Iran with an interesting problem: Patients were having headaches after pacemaker implants. Working together to investigate, they began to wonder if the underlying issue is the materials used in the pacemakers.
“Managing external noise that affects patients is crucial,” author Baraa Chasib Mezher said. “For example, a person with a brain pacemaker may experience interference from external electrical fields from phones or the sounds of cars, as well as various electromagnetic forces present in daily life. It is essential to develop novel biomaterials for the outlet gate of brain pacemakers that can effectively handle electrical signals.”
In an article published this week in AIP Advances, Mezher, who is an Iraqi doctoral student studying in Iran, and her colleagues at the Nanostructured and Novel Materials Laboratory at the University of Tabriz created organic materials for brain and heart pacemakers, which rely on uninterrupted signal delivery to be effective.
🏛️⛩️ ✍️ Lorenzo Teppati Losè et al.
The launch of the new iPad Pro by Apple in March 2020 generated high interest and expectations for different reasons; nevertheless, one of the new features that developers and users were interested in testing was the LiDAR sensor integrated into this device (and, later on, in the iPhone 12 and 13 Pro series). The implications of using this technology are mainly related to augmented and mixed reality applications, but its deployment for surveying tasks also seems promising. In particular, the potentialities of this miniaturized and low-cost sensor embedded in a mobile device have been assessed for documentation from the cultural heritage perspective—a domain where this solution may be particularly innovative. Over the last two years, an increasing number of mobile apps using the Apple LiDAR sensor for 3D data acquisition have been released.
Large language models (LLMs) are increasingly automating tasks like translation, text classification and customer service. But tapping into an LLM’s power typically requires users to send their requests to a centralized server—a process that’s expensive, energy-intensive and often slow.
Now, researchers have introduced a technique for compressing an LLM’s reams of data, which could increase privacy, save energy and lower costs. Their findings are published on the arXiv preprint server.
The new algorithm, developed by engineers at Princeton and Stanford Engineering, works by trimming redundancies and reducing the precision of an LLM’s layers of information. This type of leaner LLM could be stored and accessed locally on a device like a phone or laptop and could provide performance nearly as accurate and nuanced as an uncompressed version.
