Lifeboat Foundation

A new batch of malicious Android apps filled with adware and malware was found on the Google Play Store that have been installed close to 10 million times on mobile devices.

The apps pose as image-editing tools, virtual keyboards, system optimizers, wallpaper changers, and more. However, their underlying functionality is to push intrusive ads, subscribe users to premium services, and steal victims’ social media accounts.

The discovery of these malicious apps comes from the Dr. Web antivirus team, who highlighted the new threats in a report published today.

Normally, robotic arms are controlled by a GUI running on a host PC, or with some kind of analog system that maps human inputs to various degrees of rotation. However, Maurizio Miscio was able to build a custom robotic arm that is completely self-contained — thanks to a companion mobile app that resides on an old smartphone housed inside a control box.

Miscio started his project by making 3D models of each piece, most of which were 3D-printed. These included the gripper, various joints that each give a single axis of rotation, and a large circular base that acts as a stable platform on which the arm can spin. He then set to work attaching five servo motors onto each rotational axis, along with a single SG90 micro servo motor for the gripper. These motors were connected to an Arduino Uno that also had an HC-05 Bluetooth® serial module for external communication.

In order to operate the arm, Miscio developed a mobile app with the help of MIT App Inventor, which presents the user with a series of buttons that rotate a particular servo motor to the desired degree. The app even lets a series of motion be recorded and “played back” to the Uno over Bluetooth for repeated, accurate movements.

The biggest benchmarking data set to date for a machine learning technique designed with data privacy in mind has been released open source by researchers at the University of Michigan.

Called federated learning, the approach trains learning models on end-user devices, like smartphones and laptops, rather than requiring the transfer of private data to central servers.

“By training in-situ on data where it is generated, we can train on larger real-world data,” explained Fan Lai, U-M doctoral student in computer science and engineering, who presents the FedScale training environment at the International Conference on Machine Learning this week.

Digital information is everywhere in the era of smart technology, where data is continuously generated by and communicated among cell phones, smart watches, cameras, smart speakers and other devices. Securing digital data on handheld devices requires massive amounts of energy, according to an interdisciplinary group of Penn State researchers, who warn that securing these devices from bad actors is becoming a greater concern than ever before.

Led by Saptarshi Das, Penn State associate professor of engineering science and mechanics, researchers developed a smart hardware platform, or chip, to mitigate energy consumption while adding a layer of security. The researchers published their results on June 23 in Nature Communications.

“Information from our devices is currently stored in one location, the cloud, which is shared and stored in large servers,” said Das, who also is affiliated with the Penn State School of Electrical Engineering and Computer Science, the Materials Research Institute and the College of Earth and Mineral Sciences’ Department of Materials Science and Engineering. “The security strategies employed to store this information are extremely energy inefficient and are vulnerable to data breaches and hacking.”

In November last year, an undercover agent with the FBI was inside a group on Amazon-owned messaging app Wickr, with a name referencing young girls. The group was devoted to sharing child sexual abuse material (CSAM) within the protection of the encrypted app, which is also used by the U.S. government, journalists and activists for private communications. Encryption makes it almost impossible for law enforcement to intercept messages sent over Wickr, but this agent had found a way to infiltrate the chat, where they could start piecing together who was sharing the material.

As part of the investigation into the members of this Wickr group, the FBI used a previously unreported search warrant method to force one member to unlock the encrypted messaging app using his face. The FBI has previously forced users to unlock an iPhone with Face ID, but this search warrant, obtained by Forbes, represents the first known public record of a U.S. law enforcement agency getting a judge’s permission to unlock an encrypted messaging app with someone’s biometrics.

According to the warrant, the FBI first tracked down the suspect by sending a request for information, via an unnamed foreign law enforcement partner, to the cloud storage provider hosting the illegal images. That gave them the Gmail address the FBI said belonged to Christopher Terry, a 53-year-old Knoxville, Tennessee resident, who had prior convictions for possession of child exploitation material. It also provided IP addresses used to create the links to the CSAM. From there, investigators asked Google and Comcast via administrative subpoenas (data requests that don’t have the same level of legal requirements as search warrants) for more identifying information that helped them track down Terry and raid his home.

Google’s Threat Analysis Group (TAG), whose primary goal is to defend Google users from state-sponsored attacks, said today that Russian-backed threat groups are still focusing their attacks on Ukrainian organizations.

In a report regarding recent cyber activity in Eastern Europe, Google TAG security engineer Billy Leonard revealed that hackers part of the Turla Russian APT group have also been spotted deploying their first Android malware.

They camouflaged it as a DDoS attack tool and hosted it on cyberazov[.]com, a domain spoofing the Ukrainian Azov Regiment.

Researchers at the SketchX, University of Surrey have recently developed a meta learning-based model that allows users to retrieve images of specific items simply by sketching them on a tablet, smartphone, or on other smart devices. This framework was outlined in a paper set to be presented at the European Conference on Computer Vision (ECCV), one of the top three flagship computer vision conferences along with CVPR and ICCV.

Researchers at the SketchX, University of Surrey have recently developed a meta learning-based model that allows users to retrieve images of specific items simply by sketching them on a tablet, smartphone, or on other smart devices. This framework was outlined in a paper set to be presented at the European Conference on Computer Vision (ECCV), one of the top three flagship computer vision conferences along with CVPR and ICCV.

“This is the latest along the line of work on ‘fine-grained image retrieval,’ a problem that my research lab (SketchX, which I direct and founded back in 2012) pioneered back in 2015, with a paper published in CVPR 2015 titled ‘Sketch Me That Shoe,’” Yi-Zhe Song, one of the researchers who carried out the study, told TechXplore. “The idea behind our paper is that it is often hard or impossible to conduct image retrieval at a fine-grained level, (e.g., finding a particular type of shoe at Christmas, but not any shoe).”

In the past, some researchers tried to devise models that can retrieve images based on text or voice descriptions. Text might be easier for users to produce, yet it was found only to work at a coarse level. In other words, it can become ambiguous and ineffective when trying to describe details.

Early detection and identification of pathogenic bacteria in food and water samples are essential to public health. Bacterial infections cause millions of deaths worldwide and bring a heavy economic burden, costing more than 4 billion dollars annually in the United States alone. Among pathogenic bacteria, Escherichia coli (E. coli) and other coliform bacteria are among the most common ones, and they indicate fecal contamination in food and water samples. The most conventional and frequently used method for detecting these bacteria involves culturing of the samples, which usually takes 24 hours for the final read-out and needs expert visual examination. Although some methods based on, for example, the amplification of nucleic acids, can reduce the detection time to a few hours, they cannot differentiate live and dead bacteria and present low sensitivity at low concentrations of bacteria. That is why the U.S. Environmental Protection Agency (EPA) approves no nucleic acid-based bacteria sensing method for screening water samples.

In an article recently published in ACS Photonics, a journal of the American Chemical Society (ACS), a team of scientists, led by Professor Aydogan Ozcan from the Electrical and Computer Engineering Department at the University of California, Los Angeles (UCLA), and co-workers have developed an AI-powered smart bacterial colony detection system using a thin-film transistor (TFT) array, which is a widely used technology in mobile phones and other displays.

The ultra-large imaging area of the TFT array (27 mm × 26 mm) manufactured by researchers at Japan Display Inc. enabled the system to rapidly capture the growth patterns of bacterial colonies without the need for scanning, which significantly simplified both the hardware and software design. This system achieved ~12-hour time savings compared to gold-standard culture-based methods approved by EPA. By analyzing the microscopic images captured by the TFT array as a function of time, the AI-based system could rapidly and automatically detect colony growth with a deep neural network. Following the detection of each colony, a second neural network is used to classify the bacteria species.