South Carolina

How Artificial Intelligence is progressing in mental healthcare


According to the report, suicide is among the top 20 leading causes of death worldwide. Over the years, Artificial Intelligence (AI) tools have been used to fill gaps in mental health care: be it the diagnosis or detection of the early signs of mental health issues. Now, researchers at the University of South Carolina's Viterbi School of Engineering (USC's VSE) have developed an algorithm that can identify individuals in real-life social groups who can be trained as gatekeepers to spot suicidal tendencies. "Gatekeeper training" is an intervention training method approved by WHO. A suicide prevention gatekeeper can be any community member.

Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control Artificial Intelligence

Adaptive signal control system (ASCS) is the most advanced t raffic signal technology that regulates the signal phasing and timings considering the traffic patterns in real-time in order to reduce traffic congestion. Real-time prediction of traffic queue length can be used to adj ust the signal phasing and timings for different traffic movements at a signalized intersection with A SCS. The accuracy of the queue length prediction model varies based on the many factors, such as th e stochastic nature of the vehicle arrival rates at an intersection, time of the day, weather and driver characteristics. In addition, accurate queue length prediction for multilane, undersaturated and satur ated traffic scenarios at signalized intersections is challenging. Thus, the objective of this study is to devel op short-term queue length prediction models for signalized intersections that can be leveraged by adapt ive traffic signal control systems using four variations of Grey systems: (i) the first order single variab le Grey model (GM(1,1)); (ii) GM(1,1) with Fourier error corrections (EGM); (iii) the Grey Verhulst mo del (GVM), and (iv) GVM with Fourier error corrections (EGVM). The efficacy of the Grey models is th at they facilitate fast processing; as these models do not require a large amount of data; as would be needed in artificial intelligence models; and they are able to adapt to stochastic changes, unlike stat istical models. We have conducted a case study using queue length data from five intersections with ad aptive traffic signal control on a calibrated roadway network in Lexington, South Carolina. Grey models w ere compared with linear, nonlinear time series models, and long short-term memory (LSTM) neura l network. Based on our analyses, we found that EGVM reduces the prediction error over closest co mpeting models (i.e., LSTM and Additive Autoregressive (AAR) time series models) in predicting ave rage and maximum queue lengths by 40% and 42%, respectively, in terms of Root Mean Squared Error (R MSE), and 51% and 50%, respectively, in terms of Mean Absolute Error (MAE).

The top 3 companies in autonomous vehicles and self-driving cars ZDNet


This ebook, based on the latest ZDNet / TechRepublic special feature, examines how driverless cars, trucks, semis, delivery vehicles, drones, and other UAVs are poised to unleash a new level of automation in the enterprise. Imagine: After a long flight home from a conference, you walk outside to the airport's ground transportation and are met by an airport shuttle, which takes you directly to your car. Here's the plot twist: The shuttle is operating without a human driver. This hypothetical scenario will most likely become reality within the next five years, said Bryant Walker Smith, a University of South Carolina law professor who studies autonomous vehicles. "At the societal level, self-driving cars have the potential to save millions of lives, reshape our cities, reduce emissions, give back billions of hours of time and restore freedom of movement," said Mo ElShenawy, vice president of engineering at GM Cruise.

Big data, artificial intelligence to support research on harmful blue-green algae


A team of scientists from research centers stretching from Maine to South Carolina will develop and deploy high-tech tools to explore cyanobacteria in lakes across the East Coast. The multi-year project will combine big data, artificial intelligence and robotics with new and time-tested techniques for lake sampling to understand where, when, and how cyanobacterial blooms develop. The research team brings together experts in freshwater ecology, computer science, engineering and geospatial science from Bates College, Colby College, Dartmouth, the University of New Hampshire, the University of Rhode Island and the University of South Carolina. "It is rare to have teams from so many different specialties converge to study a problem like this," said Alberto Quattrini Li, an assistant professor of computer science at Dartmouth and the overall project lead. "By working together, we can increase the amount of data that can be collected and increase prediction capabilities."

Augmenting intelligence: How BMW's US IT center is putting AI into the hands of workers


Outside Germany, one of BMW's key research organizations for business and manufacturing technology is the IT Innovation and Research Center. With bases both in Silicon Valley, as well as Greenville, South Carolina, the IT center carries out research for systems and tools across the enterprise, including financial services, sales and marketing, engineering, quality, HR, production and logistics. It is part of the carmaker's central BMW Group IT department led from Munich, which coordinates the company's enterprise and manufacturing IT backbone. Similar to other laboratory locations across BMW, the IT center operates to a large extent in research mode. It has a strong connection, for example, to Clemson University, with whom it shares a campus at the International Center for Automotive Research, working closely with engineering and software professors and students.

University of South Carolina announces AI institute EdScoop


The University of South Carolina announced plans last week to open an artificial intelligence institute that give students and faculty a shared space for interdisciplinary collaboration. The institute, which the university hopes to have running by this fall, will focus on research to advance AI applications across a wide range of industries, Hossein Haj-Hariri, dean of the College of Engineering and Computing, told EdScoop. "[Industries] are already being transformed or will be transformed by artificial intelligence," Haj-Hariri said. "The window where we can really lead the injection of research into application areas is open," he said. To drive innovation and develop cutting-edge solutions using AI, the institute will draw on the knowledge and experience of students and faculty from all 15 colleges across the university's campus, making it a hub for interdisciplinary collaboration.

USC to open "smart data" artificial intelligence institute


The University of South Carolina wants to start working smarter. Later this summer, the school will open an institute dedicated to studying and developing artificial intelligence, which is sometimes abbreviated AI, the school announced Tuesday. The institute aims to use its AI research to help develop "self-improving" and customized programs for social workers, pharmacists, teachers and more, the release said. To do that, "The AI Institute plans to enlist philosophers, ethicists, public policy experts, and lawyers dedicated to exploring the societal impact of AI technology, both the good and the unintended negative outcomes," the release said. "For example, some have expressed concern that autonomous vehicles could soon put tens of thousands of truck drivers out of work."

Twitter Speaks: A Case of National Disaster Situational Awareness Machine Learning

In recent years, we have been faced with a series of natural disasters causing a tremendous amount of financial, environmental, and human losses. The unpredictable nature of natural disasters' behavior makes it hard to have a comprehensive situational awareness (SA) to support disaster management. Using opinion surveys is a traditional approach to analyze public concerns during natural disasters; however, this approach is limited, expensive, and time-consuming. Luckily the advent of social media has provided scholars with an alternative means of analyzing public concerns. Social media enable users (people) to freely communicate their opinions and disperse information regarding current events including natural disasters. This research emphasizes the value of social media analysis and proposes an analytical framework: Twitter Situational Awareness (TwiSA). This framework uses text mining methods including sentiment analysis and topic modeling to create a better SA for disaster preparedness, response, and recovery. TwiSA has also effectively deployed on a large number of tweets and tracks the negative concerns of people during the 2015 South Carolina flood.

Pentagon documents the military's growing domestic drone use


The US military used the MQ-9 Reaper in five domestic operations in FY 2018. The Pentagon this month published new data on its domestic drone use, documenting 11 missions during the 2018 fiscal year. That's up from 11 missions over the entire span of 2011 through 2017, as noted by Dan Gettinger, co-director of Bard College's Center for the Study of the Drone. Most of the military's 2018 missions fell under the category of "Defense Support of Civil Authorities." That meant responding to requests from the governors of California and Oregon for support during last year's wildfire season, as well as helping the South Carolina National Guard with its Hurricane Florence flood response.

Officials: Inmates Ran $560K Online Dating Extortion Scheme

U.S. News

Inmates aren't allowed to have cellphones behind bars, although thousands are smuggled inside each year. Corrections Director Bryan Stirling has long called illegally obtained cellphones the No. 1 security threat inside his institutions, as they allow inmates the unmonitored ability to communicate and potentially continue their criminal endeavors.