A team of computer scientists led by the University of Colorado Boulder has developed a new artificial intelligence platform that automatically seeks out “questionable” scientific journals.

The study, published Aug. 27 in the journal Science Advances, tackles an alarming trend in the world of research.

Daniel Acuña, lead author of the study and associate professor in the Department of Computer Science, gets a reminder of that several times a week in his email inbox: These spam messages come from people who purport to be editors at scientific journals, usually ones Acuña has never heard of, and offer to publish his papers—for a hefty fee.

Such publications are sometimes referred to as “predatory” journals. They target scientists, convincing them to pay hundreds or even thousands of dollars to publish their research without proper vetting.

“There has been a growing effort among scientists and organizations to vet these journals,” Acuña said. “But it’s like whack-a-mole. You catch one, and then another appears, usually from the same company. They just create a new website and come up with a new name.”

His group’s new AI tool automatically screens scientific journals, evaluating their websites and other online data for certain criteria: Do the journals have an editorial board featuring established researchers? Do their websites contain a lot of grammatical errors?

Acuña emphasizes that the tool isn’t perfect. Ultimately, he thinks human experts, not machines, should make the final call on whether a journal is reputable.

But in an era when prominent figures are questioning the legitimacy of science, stopping the spread of questionable publications has become more important than ever before, he said.

“In science, you don’t start from scratch. You build on top of the research of others,” Acuña said. “So if the foundation of that tower crumbles, then the entire thing collapses.”

The shake down

When scientists submit a new study to a reputable publication, that study usually undergoes a practice called peer review. Outside experts read the study and evaluate it for quality—or, at least, that’s the goal.

A growing number of companies have sought to circumvent that process to turn a profit. In 2009, Jeffrey Beall, a librarian at CU Denver, coined the phrase “predatory” journals to describe these publications.

Often, they target researchers outside of the United States and Europe, such as in China, India and Iran—countries where scientific institutions may be young, and the pressure and incentives for researchers to publish are high.

“They will say, ‘If you pay $500 or $1,000, we will review your paper,'” Acuña said. “In reality, they don’t provide any service. They just take the PDF and post it on their website.”

A few different groups have sought to curb the practice. Among them is a nonprofit organization called the Directory of Open Access Journals (DOAJ). Since 2003, volunteers at the DOAJ have flagged thousands of journals as suspicious based on six criteria. (Reputable publications, for example, tend to include a detailed description of their peer review policies on their websites.)

But keeping pace with the spread of those publications has been daunting for humans.

To speed up the process, Acuña and his colleagues turned to AI. The team trained its system using the DOAJ’s data, then asked the AI to sift through a list of nearly 15,200 open-access journals on the internet.

Among those journals, the AI initially flagged more than 1,400 as potentially problematic.

Acuña and his colleagues asked human experts to review a subset of the suspicious journals. The AI made mistakes, according to the humans, flagging an estimated 350 publications as questionable when they were likely legitimate. That still left more than 1,000 journals that the researchers identified as questionable.

“I think this should be used as a helper to prescreen large numbers of journals,” he said. “But human professionals should do the final analysis.”

Acuña added that the researchers didn’t want their system to be a “black box” like some other AI platforms.

“With ChatGPT, for example, you often don’t understand why it’s suggesting something,” Acuña said. “We tried to make ours as interpretable as possible.”

The team discovered, for example, that questionable journals published an unusually high number of articles. They also included authors with a larger number of affiliations than more legitimate journals, and authors who cited their own research, rather than the research of other scientists, to an unusually high level.

The new AI system isn’t publicly accessible, but the researchers hope to make it available to universities and publishing companies soon. Acuña sees the tool as one way that researchers can protect their fields from bad data—what he calls a “firewall for science.”

“As a computer scientist, I often give the example of when a new smartphone comes out,” he said. “We know the phone’s software will have flaws, and we expect bug fixes to come in the future. We should probably do the same with science.”

One of the lesser-known apps in the Google Drive online suite is Google Forms. It’s an easy, intuitive way to create a web form for other people to enter information into. You can use it for employee surveys, for organizing social gatherings, for giving people a way to contact you, and much more. But Google Forms can also be used for malicious purposes.

These forms can be created in minutes, with clean and clear formatting, official-looking images and video, and—most importantly of all—a genuine Google Docs URL that your web browser will see no problem with. Scammers can then use these authentic-looking forms to ask for payment details or login information.

It’s a type of scam that continues to spread, with Google itself issuing a warning about the issue in February. Students and staff at Stanford University were among those targeted with a Google Forms link that asked for login details for the academic portal there, and the attack beat standard email malware protection.

How the Scam Works

These scams can take a variety of guises, but they’ll typically start with a phishing email that will try to trick you into believing it’s an official and genuine communication. It might be designed to look like it’s from a colleague, an administrator, or someone from a reputable organization.

The apparent quality and trustworthiness of this original phishing email is part of the con. Our inboxes are regularly filled with requests to reset passwords, verify details, or otherwise take action. Like many scams, the email might suggest a sense or urgency, or indicate that your security has been compromised in some way.

Even worse, the instigating email might actually come from a legitimate email address, if someone in your social circle, family, or office has had their account hijacked. In this case you wouldn’t be able to run the usual checks on the sender identity and email address, because everything would look genuine—though the wording and style would be off.

This email (or perhaps a direct message on social media) will be used to deliver a Google Forms link, which is the second half of the scam. This form will most often be set up to look genuine, and may be trying to spoof a recognized site like your place of work or your bank. The form might prompt you for sensitive information, offer up a link to malware, or feature a phone number or email address to lead you into further trouble.

The rapid advancement of artificial intelligence (AI) and machine learning systems has increased the demand for new hardware components that could speed up data analysis while consuming less power. As machine learning algorithms draw inspiration from biological neural networks, some engineers have been working on hardware that also mimics the architecture and functioning of the human brain.

Brain-inspired, or neuromorphic, hardware typically integrates components that mimic the functioning of brain cells, which are thus referred to as artificial neurons. Artificial neurons are connected to one another, with their connections weakening or strengthening over time.

This process resembles synaptic plasticity, the ability of the brain to adapt over time in response to experience and learning. By emulating synaptic plasticity, neuromorphic computing systems could run machine learning algorithms more efficiently, consuming less energy when analyzing large amounts of data and making predictions.

Researchers at Fudan University have recently developed a device based on the ultrathin semiconductor monolayer molybdenum disulfide (MoS₂) that could emulate the adaptability of biological neurons better than other artificial neurons introduced in the past. The new system, introduced in a paper published in Nature Electronics, combines a type of computer memory known as dynamic random-access memory (DRAM) with MoS₂-based circuits.

“Neuromorphic hardware that accurately simulates diverse neuronal behaviors could be of use in the development of edge intelligence,” Yin Wang, Saifei Gou and their colleagues wrote in their paper.

“Hardware that incorporates synaptic plasticity—adaptive changes that strengthen or weaken synaptic connections—has been explored, but mimicking the full spectrum of learning and memory processes requires the interplay of multiple plasticity mechanisms, including intrinsic plasticity. We show that an integrate-and-fire neuron can be created by combining a dynamic random-access memory and an inverter that are based on wafer-scale monolayer molybdenum disulfide films.”

The artificial neuron developed by the researchers has two key components: a DRAM system and an inverter circuit. DRAMs are memory systems that can store electrical charges in structures known as capacitors. The amount of electrical charge in the capacitors can be modulated to mimic variations in the electrical charge across the membrane of biological neurons, which ultimately determine whether they will fire or not.

An inverter, on the other hand, is an electronic circuit that can flip an input signal from high voltage to low voltage or vice versa. In the team’s artificial neuron, this circuit enables the generation of bursts of electricity resembling those observed in biological neurons when they fire.

“In the system, the voltage in the dynamic random-access memory capacitor—that is, the neuronal membrane potential—can be modulated to emulate intrinsic plasticity,” wrote the authors. “The module can also emulate the photopic and scotopic adaptation of the human visual system by dynamically adjusting its light sensitivity.”

To assess the potential of the artificial neuron they created, the researchers fabricated a few and assembled them into a 3 × 3 grid. They then tested the ability of this 3×3 neuron array to adapt its responses to inputs based on changes in light, mimicking how the human visual system adapts in different lighting conditions. Finally, they used their system to run a model for image recognition and assessed its performance.

“We fabricate a 3 × 3 photoreceptor neuron array and demonstrate light coding and visual adaptation,” wrote the authors. “We also use the neuron module to simulate a bioinspired neural network model for image recognition.”

The artificial neuron developed by Wang, Gou and their colleagues has proved to be very promising so far, particularly for the energy-efficient implementation of computer vision and image recognition models. In the future, the researchers could fabricate other bio-inspired computing systems based on the newly developed device and test their performance on other computational tasks.

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