Analyzing Factors Influencing Driver Willingness to Accept Advanced Driver Assistance Systems Hannah Musau a,, Nana Kankam Gyimah a, Judith Mwakalonge a, Gurcan Comert b, Saidi Siuhi a a Department of Engineering, South Carolina State University, Orangeburg, South Carolina, USA, 29117 b Department of Computational Engineering and Data Science, North Carolina A&T State University, Greensboro, North Carolina, US, 27411Abstract Advanced Driver Assistance Systems (ADAS) enhance highway safety by improving environmental perception and reducing human errors. However, misconceptions, trust issues, and knowledge gaps hinder widespread adoption. This study examines driver perceptions, knowledge sources, and usage patterns of ADAS in passenger vehicles. A nationwide survey collected data from a diverse sample of U.S. drivers. Machine learning models predicted ADAS adoption, with SHAP (SHapley Additive Explanations) identifying key influencing factors. Findings indicate that higher trust levels correlate with increased ADAS usage, while concerns about reliability remain a barrier. Findings emphasize the influence of socioeconomic, demographic, and behavioral factors on ADAS adoption, offering guidance for automakers, policymakers, and safety advocates to improve awareness, trust, and usability. Introduction Human factors are the leading cause of road crashes, contributing to over 90% of incidents either alone or alongside failures in vehicles or infrastructure [1].

The use of biometrics to authenticate users and control access to secure areas has become extremely popular in recent years, and biometric access control systems are frequently used by both governments and private corporations. However, these systems may represent risks to security when deployed without considering the possibility of biometric presentation attacks (also known as spoofing). Presentation attacks are a serious threat because they do not require significant time, expense, or skill to carry out while remaining effective against many biometric systems in use today. This research compares three different software-based methods for facial and iris presentation attack detection in images. The first method uses Inception-v3, a pre-trained deep Convolutional Neural Network (CNN) made by Google for the ImageNet challenge, which is retrained for this problem. The second uses a shallow CNN based on a modified Spoofnet architecture, which is trained normally. The third is a texture-based method using Local Binary Patterns (LBP). The datasets used are the ATVS-FIr dataset, which contains real and fake iris images, and the CASIA Face Anti-Spoofing Dataset, which contains real images as well as warped photos, cut photos, and video replay presentation attacks. We also present a third set of results, based on cropped versions of the CASIA images.

GREENSBORO, N.C.--(BUSINESS WIRE)--Dais X, a leading digital transformation partner to middle-market companies, today announces that it intends to merge its consulting and software product development platform with Dais AI, an artificial intelligence (AI) company purpose-built to help clients capitalize on digital business opportunities through the development of custom AI solutions and technologies. "As shown by our steady growth over the past three years, Dais X continues to gain momentum, marked by strong progress on our strategic initiatives and client successes," said Neal Davis, President and Chief Executive Officer. "With these foundations in place, we are now ideally positioned to merge with Dais AI and create one leading company. In alignment with our strategic plan, the decision to combine these businesses will allow Dais X to sharpen its focus as a leading digital transformation partner to middle-market companies." Dais X believes the merger will sharpen the company's focus as a leading digital transformation partner, encouraging long-term revenue growth and margin expansion.

Suky Kang and Daniel Jun Lee were married Sept. 29 at Volunteer Park in Seattle. Gholamreza Assadi, the bride's brother-in-law who became a Universal Life minister for this event, officiated. Ms. Kang, 29, is the director of international programs at Code.org, a nonprofit organization in Seattle that champions computer science education in kindergarten through Grade 12. She graduated from Harvard and received an master's of arts degree from the Bard Graduate Center in New York. She is a daughter of Ki Ok Kang and Shin Woo Kang of Greensboro, N.C.

This paper discusses how AI models of argumentation can play a role in personalized and participatory medicine. It describes our previous research on natural language generation of argumentation for genetic counseling and a pilot study on risk visualization, and our current research on argumentation mining.

The challenges of effective health risk communication are well known. This paper provides pointers to the health communication literature that discuss these problems. Tailoring printed information, visual displays, and interactive multimedia have been proposed in the health communication literature as promising approaches. On-line risk communication applications are increasing on the internet. However, potential effectiveness of applications using conventional computer technology is limited. We propose that use of artificial intelligence, building upon research in Intelligent Tutoring Systems, might be able to overcome these limitations.

The objective of our research is to facilitate social conversation between persons affected with Alzheimer’s Disease (AD) and their caregivers via a future intervention for caregivers. In the intervention, a computer system will enable caregivers to practice spoken conversation with high-fidelity Virtual Patients simulating the verbal and non-verbal behavior of persons with AD (VP-AD). It is hoped that the skills acquired by the caregiver will improve the quality of life of persons with AD and reduce caregiver stress. In this paper, we describe a pilot study intended to evaluate the efficacy of active participation in conversation with a lower fidelity VP-AD in comparison to passive observation of the same VP-AD in conversation. The study found, after 15 minutes or less of practice, a significant increase in use of recommended caregiver communication skills by participants in the active condition.

Microelectronics supplier TRW optimizes the combustion process Clothing manufacturer Wrangler (Redondo Beach, CA) is using virtual in a coal-fired utility boiler, (Greensboro, NC) has developed a reality (VR) to decontaminate nuclear reducing nitrogen oxide emissions neural network system to improve facilities. The company has developed and loss on ignition while improving production planning and forecasting. An applications to its 36,000 Group (Washington, DC) has expert system makes recommendations employees worldwide. Pacific Gas & Electric (PG&E) (San provides real-time restoration of NeuralWare (Pittsburgh, PA), a Francisco, CA), a public utility, has telecommunications services in areas provider of neural network software, affected by disaster or accidents. The system allows PG&E outage through a series of tests, 24 for target and path optimization to offer customers flexible energy hours a day, 7 days a week.