Generative AI paraphrased text can be used for copyright infringement and the AI paraphrased content can deprive substantial revenue from original content creators. Despite this recent surge of malicious use of generative AI, there are few academic publications that research this threat. In this article, we demonstrate the ability of pattern-based similarity detection for AI paraphrased news recognition. We propose an algorithmic scheme, which is not limited to detect whether an article is an AI paraphrase, but, more importantly, to identify that the source of infringement is the ChatGPT. The proposed method is tested with a benchmark dataset specifically created for this task that incorporates real articles from BBC, incorporating a total of 2,224 articles across five different news categories, as well as 2,224 paraphrased articles created with ChatGPT. Results show that our pattern similarity-based method, that makes no use of deep learning, can detect ChatGPT assisted paraphrased articles at percentages 96.23% for accuracy, 96.25% for precision, 96.21% for sensitivity, 96.25% for specificity and 96.23% for F1 score.

Despite the great potential of large language models(LLMs) in machine comprehension, it is still disturbing to fully count on them in real-world scenarios. This is probably because there is no rational explanation for whether the comprehension process of LLMs is aligned with that of experts. In this paper, we propose SCOP to carefully examine how LLMs perform during the comprehension process from a cognitive view. Specifically, it is equipped with a systematical definition of five requisite skills during the comprehension process, a strict framework to construct testing data for these skills, and a detailed analysis of advanced open-sourced and closed-sourced LLMs using the testing data. With SCOP, we find that it is still challenging for LLMs to perform an expert-level comprehension process. Even so, we notice that LLMs share some similarities with experts, e.g., performing better at comprehending local information than global information. Further analysis reveals that LLMs can be somewhat unreliable -- they might reach correct answers through flawed comprehension processes. Based on SCOP, we suggest that one direction for improving LLMs is to focus more on the comprehension process, ensuring all comprehension skills are thoroughly developed during training.

We propose two neural network based and data-driven supply and demand models to analyze the efficiency, identify service gaps, and determine the significant predictors of demand, in the bus system for the Department of Public Transportation (HDPT) in Harrisonburg City, Virginia, which is the home to James Madison University (JMU). The supply and demand models, one temporal and one spatial, take many variables into account, including the demographic data surrounding the bus stops, the metrics that the HDPT reports to the federal government, and the drastic change in population between when JMU is on or off session. These direct and data-driven models to quantify supply and demand and identify service gaps can generalize to other cities' bus systems. Keywords-- transportation systems, bus systems, public transportation, direct ridership models, data driven models, mathematical modeling, neural networks, machine learning, supply models, demand models, machine learning, service gaps, social vulnerability, public transportation access, GIS data, data science, data quality.

Think your kid is addicted to video games? There could be something to it. The World Health Organization has made video game addiction an official mental health disorder. The Geneva, Switzerland-headquartered organization has added "Gaming disorder" to the International Statistical Classification of Diseases and Related Health Problems or "ICD-11," which goes into effect in January 2022. The condition has been included in the global medical guide within a section detailing disorders due to substance use or addictive behaviors along with "Gambling disorder." The ICD-11 describes "Gaming disorder" as recurrent video game playing that leads to "impaired control over gaming" and an "increasing priority given to gaming to the extent that gaming takes precedence over other life interests and daily activities," despite "the occurrence of negative consequences."

The World Health Organization says that compulsively playing video games now qualifies as a new mental health condition, in a move that some critics warn may risk stigmatizing too many young players. While it's hard for some investors to wrap their arms around the significance of gaming, the short answer is it is massive and expected to continue to exhibit huge growth for years to come. Can someone truly be addicted to video games? The World Health Organization thinks so – but a major professional organization for psychiatrists strongly disagrees. The World Health Organization on Monday classified "gaming disorder" as a diagnosable condition, giving mental health professionals a basis for setting up treatment and identifying risks for the addictive behavior.

As always for a presidential inaugural, security and surveillance were extremely tight in Washington, DC, last January. But as George W. Bush prepared to take the oath of office, security planners installed an extra layer of protection: a prototype software system to detect a biological attack. The U.S. Department of Defense, together with regional health and emergency-planning agencies, distributed a special patient-query sheet to military clinics, civilian hospitals and even aid stations along the parade route and at the inaugural balls. Software quickly analyzed complaints of seven key symptoms-from rashes to sore throats-for patterns that might indicate the early stages of a bio-attack. There was a brief scare: the system noticed a surge in flulike symptoms at military clinics.