The integration of social media and artificial intelligence (AI) into disaster management, particularly for earthquake response, represents a profound evolution in emergency management practices. In the digital age, real-time information sharing has reached unprecedented levels, with social media platforms emerging as crucial communication channels during crises. This shift has transformed traditional, centralized emergency services into more decentralized, participatory models of disaster situational awareness. Our study includes an experimental analysis of 8,900 social media interactions, including 2,920 posts and 5,980 replies on X (formerly Twitter), following a magnitude 5.1 earthquake in Oklahoma on February 2, 2024. The analysis covers data from the immediate aftermath and extends over the following seven days, illustrating the critical role of digital platforms in modern disaster response. The results demonstrate that social media platforms can be effectively used as real-time situational awareness tools, delivering critical information to society and authorities during emergencies.

Mike Creef is a fighter for equality and justice for all. Growing up bi-racial (Jamaican-American) on the east coast allowed him to experience many different cultures and beliefs that helped give him a well-rounded worldview. After playing a year of college basketball, he moved to Tulsa, OK in 2008 to pursue training at bible college. He quickly fell in love with serving the people of the city as well as city engagement. He has worked and volunteered in both the public and private sectors and currently enjoys writing to inspire and challenge people to see that there is more that unites us than divides us.

Federated learning (FL) provides a high efficient decentralized machine learning framework, where the training data remains distributed at remote clients in a network. Though FL enables a privacy-preserving mobile edge computing framework using IoT devices, recent studies have shown that this approach is susceptible to poisoning attacks from the side of remote clients. To address the poisoning attacks on FL, we provide a \textit{two-phase} defense algorithm called {Lo}cal {Ma}licious Facto{r} (LoMar). In phase I, LoMar scores model updates from each remote client by measuring the relative distribution over their neighbors using a kernel density estimation method. In phase II, an optimal threshold is approximated to distinguish malicious and clean updates from a statistical perspective. Comprehensive experiments on four real-world datasets have been conducted, and the experimental results show that our defense strategy can effectively protect the FL system. {Specifically, the defense performance on Amazon dataset under a label-flipping attack indicates that, compared with FG+Krum, LoMar increases the target label testing accuracy from $96.0\%$ to $98.8\%$, and the overall averaged testing accuracy from $90.1\%$ to $97.0\%$.

BOK Financial has spent the last few years refining its recruitment strategy. One motivation is to diversify its employee base. The Tulsa, Okla., company has updated job descriptions to remove gender bias and revamped its interview process to be more structured. It also enhanced its training: In 2016, every recruiter completed a Certified Diversity and Inclusion Recruiter training program run by AIRS, a division of payroll company ADP. BOK rolled out its own six-month diversity and inclusion accreditation program last year, which focuses on understanding inclusion and unconscious bias.

The exponential rise in mobile traffic originating from mobile devices highlights the need for making mobility management in future networks even more efficient and seamless than ever before. Ultra-Dense Cellular Network vision consisting of cells of varying sizes with conventional and mmWave bands is being perceived as the panacea for the eminent capacity crunch. However, mobility challenges in an ultra-dense heterogeneous network with motley of high frequency and mmWave band cells will be unprecedented due to plurality of handover instances, and the resulting signaling overhead and data interruptions for miscellany of devices. Similarly, issues like user tracking and cell discovery for mmWave with narrow beams need to be addressed before the ambitious gains of emerging mobile networks can be realized. Mobility challenges are further highlighted when considering the 5G deliverables of multi-Gbps wireless connectivity, <1ms latency and support for devices moving at maximum speed of 500km/h, to name a few. Despite its significance, few mobility surveys exist with the majority focused on adhoc networks. This paper is the first to provide a comprehensive survey on the panorama of mobility challenges in the emerging ultra-dense mobile networks. We not only present a detailed tutorial on 5G mobility approaches and highlight key mobility risks of legacy networks, but also review key findings from recent studies and highlight the technical challenges and potential opportunities related to mobility from the perspective of emerging ultra-dense cellular networks.

LittleBigBrain, LLC announced today the launch of their Artificial Intelligence (AI) software company. The founders, John Morad and Kevin Malone, have both built highly successful enterprise-level software organizations from the ground-up. They are excited about the endless range of what AI can accomplish. "We see extraordinary opportunities in AI and feel we are uniquely positioned to capitalize on what we view as just the beginning of a rapid upward growth trajectory in the industry," said Kevin Malone, CEO of LittleBigBrain. "The potential of what this technology can do is limitless, and we are currently just scratching the surface."

Two mayors discussed how they are using artificial intelligence and machine learning to improve their cities and prepare for the workforce of the future at a conference held April 23 in Chicago. The event was hosted by news organization Axios and the United States Conference of Mayors and led by Axios Executive Editor Mike Allen. Also joining the discussion was Imir Arifi, head of artificial intelligence and machine learning at Health Care Service Corporation. According to Arifi, the main use of AI and machine learning is through historical data to predict future events. In a city, for example, Arifi said AI can be used to predict how many potholes the city will need to fill in a year based on data from previous years.

Abbott Analytics, provides data mining consulting, knowledge transfer, and training for direct marketing, fraud detection, bioinformatics, and scientific computing. Algoritmica, providing consultancy and customized predictive analytics solutions for a number of international companies. Altius, specializes in the design and building of business-critical information systems that enhance business intelligence (BI) and performance management. Analytica, a consulting and IT firm serving US public and private sector enterprises focused on national security, law enforcement, health care and financial services. Analytics Advisory Group, offers services to improve your business outcomes by providing advisory, consulting, and training services anchored in Analytics. Analytical People offers a range of services, and resources, to any organisations who are looking to deploy Data Mining, Predictive Analytics or Statistical Analysis tools or methods. Anderson Analytics, focuses on helping clients gain the "Information Advantage" via quantitative and qualitative solutions to challenging marketing problems. Anthem Marketing Solutions, marketing and media strategists armed with the analytical capabilities and product solutions you need to deliver on your goals. Apteco, consultation and advice on the use of Faststats data mining to improve business insight and marketing campaigns. ASID Analytics provides data science consulting services, Tulsa, OK, USA. Austin Provider Solution, healthcare and managed care business intelligence solutions, including DSS for Hedis. Bayesia, providing consulting and customized solutions for computer-aided decision making, specializing in Bayesian Networks. Bentley University Center for Quantitative Analysis, provides professional analytical consulting services in support of fundamental and applied business research. Beyond the Arc, Inc., a strategic consultancy specializing in Voice of the Customer; uses analytics and text mining to translate customer data into knowledge, making customer experience more meaningful.

Google is known for celebrating holidays through its interactive Doodles and Easter eggs. To honor April Fools' Day, Google added Ms. Pac-Man to Google Maps. This isn't the first time Google turns Google Maps into a Pac-Man game, the company did the same thing in 2015. Depending on the location when you open Google Maps, the game will be played over the area. I first played in Tulsa, Oklahoma, then in Midtown Manhattan around Times Square, which is convenient since the city blocks are perfectly aligned.

Social media has recently emerged as a premier method to disseminate information online. Through these online networks, tens of millions of individuals communicate their thoughts, personal experiences, and social ideals. We therefore explore the potential of social media to predict, even prior to onset, Major Depressive Disorder (MDD) in online personas. We employ a crowdsourced method to compile a list of Twitter users who profess to being diagnosed with depression. Using up to a year of prior social media postings, we utilize a Bag of Words approach to quantify each tweet. Lastly, we leverage several statistical classifiers to provide estimates to the risk of depression. Our work posits a new methodology for constructing our classifier by treating social as a text-classification problem, rather than a behavioral one on social media platforms. By using a corpus of 2.5M tweets, we achieved an 81% accuracy rate in classification, with a precision score of .86. We believe that this method may be helpful in developing tools that estimate the risk of an individual being depressed, can be employed by physicians, concerned individuals, and healthcare agencies to aid in diagnosis, even possibly enabling those suffering from depression to be more proactive about recovering from their mental health.