Experiment in one of the world's quietest rooms reveals the hairs detect airborne sounds--like predators. Breakthroughs, discoveries, and DIY tips sent six days a week. Have you ever walked into a room full of caterpillars? While the answer for most people is probably no, those of us who have may have noticed the insects reacting to the sound of your voice. That's what happened to Carol Miles, a biologist at Binghamton University in New York.

The line between human and machine authorship is blurring, particularly as it's become increasingly difficult to tell whether something was written by a person or AI. Now, in what may seem like a tipping point, the digital marketing firm Graphite recently published a study showing that more than 50% of articles on the web are being generated by artificial intelligence. As a scholar who explores how AI is built, how people are using it in their everyday lives, and how it's affecting culture, I've thought a lot about what this technology can do and where it falls short. If you're more likely to read something written by AI than by a human on the internet, is it only a matter of time before human writing becomes obsolete? Or is this simply another technological development that humans will adapt to?

This paper reports a case study of an application of high-resolution agent-based modeling and simulation to pandemic response planning on a university campus. In the summer of 2020, we were tasked with a COVID-19 pandemic response project to create a detailed behavioral simulation model of the entire campus population at Binghamton University. We conceptualized this problem as an agent migration process on a multilayer transportation network, in which each layer represented a different transportation mode. As no direct data were available about people's behaviors on campus, we collected as much indirect information as possible to inform the agents' behavioral rules. Each agent was assumed to move along the shortest path between two locations within each transportation layer and switch layers at a parking lot or a bus stop, along with several other behavioral assumptions. Using this model, we conducted simulations of the whole campus population behaviors on a typical weekday, involving more than 25,000 agents. We measured the frequency of close social contacts at each spatial location and identified several busy locations and corridors on campus that needed substantial behavioral intervention. Moreover, systematic simulations with varying population density revealed that the effect of population density reduction was nonlinear, and that reducing the population density to 40-45% would be optimal and sufficient to suppress disease spreading on campus. These results were reported to the university administration and utilized in the pandemic response planning, which led to successful outcomes.

Advancements in generative models, like Deepfake allows users to imitate a targeted person and manipulate online interactions. It has been recognized that disinformation may cause disturbance in society and ruin the foundation of trust. This article presents DeFakePro, a decentralized consensus mechanism-based Deepfake detection technique in online video conferencing tools. Leveraging Electrical Network Frequency (ENF), an environmental fingerprint embedded in digital media recording, affords a consensus mechanism design called Proof-of-ENF (PoENF) algorithm. The similarity in ENF signal fluctuations is utilized in the PoENF algorithm to authenticate the media broadcasted in conferencing tools. By utilizing the video conferencing setup with malicious participants to broadcast deep fake video recordings to other participants, the DeFakePro system verifies the authenticity of the incoming media in both audio and video channels.

It is aimed at providing industry exposure to students where experts from reputed organisations guided students to get a deeper understanding of artificial intelligence and machine learning techniques. Madhusudhan Govindaraju, Professor, Department of Computer Science and Vice Provost for International Education and Global Affairs (IEGA), Binghamton University, New York was the chief guest. He mentioned about opportunities available for MAHE students and faculty at Binghamton University. M. D. Venkatesh, Vice Chancellor, MAHE, also spoke at the event, a release mentioned.

AI and machine learning are playing an important role in fighting the pandemic brought on by COVID-19, with technological innovation and ingenuity being applied to large volumes of data to quickly identify patterns and gain insights. Efforts are underway to speed up research and treatment, and better understand how COVID-19 spreads. Chatbots employing AI are speeding up communication around the pandemic. One example is from Clevy.io, a French startup that launched a chatbot to make it easier for people to find official government communications about COVID-19, according to an account from the World Economic Forum. The bot is getting realtime information from the French government and the World Health Organization, to help relay known symptoms and answer questions about government policies.

What do these topics have in common? The answer can be found in machine learning research at Binghamton University. Dana Bani-Hani, a doctoral student studying industrial and systems engineering, has spent the past few years teaching machines how to read data sets in any industry. The system she coded, called a Recursive General Regression Neural Network Oracle (R-GRNN Oracle), takes data inputs and creates prediction outputs. Classification models are not new in data science and analytics, but what Bani-Hani created goes beyond the basics.

Men are far more aggressive on online dating sites - but often'mass mail' women that are out of their league in the hope of a response, researchers have found. They say that by comparison, women tend to be more conscious of their own attractiveness to other users and approach fewer people. The study found major behavioural differences between male and female users when it comes to contacting potential partners. The researchers say men are far more aggressive on online dating sites - but often'mass mail' women that are out of their league in the hope of a response, researchers have found. Using data collected from Baihe, one of the largest dating websites in China, researchers from Binghamton University, University of Massachusetts Lowell and Northeastern University analysed the messages and how suitable each person actually was.