Life satisfaction is a crucial facet of human well-being. Hence, research on life satisfaction is incumbent for understanding how individuals experience their lives and influencing interventions targeted at enhancing mental health and well-being. Life satisfaction has traditionally been measured using analog, complicated, and frequently error-prone methods. These methods raise questions concerning validation and propagation. However, this study demonstrates the potential for machine learning algorithms to predict life satisfaction with a high accuracy of 93.80% and a 73.00% macro F1-score. The dataset comes from a government survey of 19000 people aged 16-64 years in Denmark. Using feature learning techniques, 27 significant questions for assessing contentment were extracted, making the study highly reproducible, simple, and easily interpretable. Furthermore, clinical and biomedical large language models (LLMs) were explored for predicting life satisfaction by converting tabular data into natural language sentences through mapping and adding meaningful counterparts, achieving an accuracy of 93.74% and macro F1-score of 73.21%. It was found that life satisfaction prediction is more closely related to the biomedical domain than the clinical domain. Ablation studies were also conducted to understand the impact of data resampling and feature selection techniques on model performance. Moreover, the correlation between primary determinants with different age brackets was analyzed, and it was found that health condition is the most important determinant across all ages. This study demonstrates how machine learning, large language models and XAI can jointly contribute to building trust and understanding in using AI to investigate human behavior, with significant ramifications for academics and professionals working to quantify and comprehend subjective well-being.

As a former military intelligence officer, serving in the Defense Intelligence Agency (DIA), I tracked foreign threats to the U.S. homeland, identifying adversaries' plans, intentions and capabilities that could harm Americans. I predicted Russia's invasion of Ukraine more than a year before it took place. In March, in my Fox News Digital article titled "Ignore FBI director's urgent warning about terrorist threats at our own peril," I predicted terrorist attacks striking inside the U.S. homeland, the kind that took place on New Year's Day in New Orleans and in Las Vegas. Here are the top three reasons why we will likely face more terrorism in America this year. This time, it will be something we haven't seen before.

A significant number of traffic crashes are secondary crashes that occur because of an earlier incident on the road. Thus, early detection of traffic incidents is crucial for road users from safety perspectives with a potential to reduce the risk of secondary crashes. The wide availability of GPS devices now-a-days gives an opportunity of tracking and recording vehicle trajectories. The objective of this study is to use vehicle trajectory data for advance real-time detection of traffic incidents on highways using machine learning-based algorithms. The study uses three days of unevenly sequenced vehicle trajectory data and traffic incident data on I-10, one of the most crash-prone highways in Louisiana. Vehicle trajectories are converted to trajectories based on virtual detector locations to maintain spatial uniformity as well as to generate historical traffic data for machine learning algorithms. Trips matched with traffic incidents on the way are separated and along with other trips with similar spatial attributes are used to build a database for modeling. Multiple machine learning algorithms such as Logistic Regression, Random Forest, Extreme Gradient Boost, and Artificial Neural Network models are used to detect a trajectory that is likely to face an incident in the downstream road section. Results suggest that the Random Forest model achieves the best performance for predicting an incident with reasonable recall value and discrimination capability.

When a devastating earthquake struck Turkey in the early hours of February 6, 2023, the five-storey building in Hatay where 13-year-old Mehmet Koc lived, collapsed, burying him in rubble and killing his older brother Emre, 14, and his mother Didem. But it took 76 hours before rescuers could pull him from the mound of concrete and twisted metal that remained of his home. Later in hospital, doctors determined that his legs were so badly crushed and injured, that both needed to be amputated just below the hip. Hearing of the earthquake in London where he lived and worked, Mehmet's father, Hasan, caught the next available flight to Turkey and travelled to Hatay, in the southeast, desperate for news of his family. The 58-year-old encountered a scene of utter destruction.

New York City (NYC) topped the global chart for the worst air pollution in June 2023, owing to the wildfire smoke drifting in from Canada. This unprecedented situation caused significant travel disruptions and shifts in traditional activity patterns of NYC residents. This study utilized large-scale social media data to study different crisis activity concerns (i.e., evacuation, staying indoors, shopping, and recreational activities among others) in the emergence of the 2023 Canadian wildfire smoke in NYC. In this regard, one week (June 02 through June 09, 2023) geotagged Twitter data from NYC were retrieved and used in the analysis. The tweets were processed using advanced text classification techniques and later integrated with national databases such as Social Security Administration data, Census, and American Community Survey. Finally, a model has been developed to make community inferences of different activity concerns in a major wildfire. The findings suggest, during wildfires, females are less likely to engage in discussions about evacuation, trips for medical, social, or recreational purposes, and commuting for work, likely influenced by workplaces maintaining operations despite poor air quality. There were also racial disparities in these discussions, with Asians being more likely than Hispanics to discuss evacuation and work commute, and African Americans being less likely to discuss social and recreational activities. Additionally, individuals from low-income neighborhoods and non-higher education students expressed fewer concerns about evacuation. This study provides valuable insights for policymakers, emergency planners, and public health officials, aiding them in formulating targeted communication strategies and equitable emergency response plans.

This paper describes our approach to submissions made at Shared Task 2 at BLP Workshop - Sentiment Analysis of Bangla Social Media Posts. Sentiment Analysis is an action research area in the digital age. With the rapid and constant growth of online social media sites and services and the increasing amount of textual data, the application of automatic Sentiment Analysis is on the rise. However, most of the research in this domain is based on the English language. Despite being the world's sixth most widely spoken language, little work has been done in Bangla. This task aims to promote work on Bangla Sentiment Analysis while identifying the polarity of social media content by determining whether the sentiment expressed in the text is Positive, Negative, or Neutral. Our approach consists of experimenting and finetuning various multilingual and pre-trained BERT-based models on our downstream tasks and using a Majority Voting and Weighted ensemble model that outperforms individual baseline model scores. Our system scored 0.711 for the multiclass classification task and scored 10th place among the participants on the leaderboard for the shared task. Our code is available at https://github.com/ptnv-s/RSM-NLP-BLP-Task2 .

Fortune ranked the University of Alabama at Birmingham's in-person master's degree in cybersecurity as the No. 1 program in the country. According to Fortune, there are nearly 770,000 cybersecurity job openings in the United States. "We are proud to be recognized for academic excellence by Fortune and named the nation's leading institution for graduate studies in cybersecurity," said UAB Provost and Senior Vice President for Academic Affairs Pam Benoit. "UAB's Department of Computer Science has created an outstanding collaborative master's degree program that prepares students to lead careers solving the world's most challenging cybersecurity problems." Fortune's first-ever ranking of in-person cybersecurity master's degree programs compared 14 programs across the United States in three components: Selectivity Score, Success Score and Demand Score.

Left-wing activist Michael Moore claimed Sunday that U.S. defense policy and spending should be refocused from military involvement in other countries to instead fight climate change, White supremacy and the coronavirus pandemic. Appearing on MSNBC, Moore suggested U.S. armed forces weren't "the good guys" because of their military involvement in countries like Syria and Somalia, and instead claimed he wanted to be known for doing "the good things," like building wells in poor villages rather than provide funding to Israel's Iron Dome defense system. "As we speak tonight the U.S. still has 2,500 troops in Iraq. Last week the U.S. military admitted that a drone strike in Kabul that killed ten innocent civilians was an American drone strike. Seven of them were kids. We talk about ending the war but we're still going to carry on with drones, we're still going to carry on with these, quote, over-the-horizon operations. You know, we are still at war," host Mehdi Hasan said after playing a clip of President Joe Biden touting the U.S. not being at war for the first time in 20 years.

The human brain has always been under study for inspiration of computing systems. Although there's a very long way to go until we can achieve a computing system that matches the efficiency of the human brain for cognitive tasks, several brain-inspired computing paradigms are being researched. Convolutional neural networks are a widely used machine learning approach for AI-related applications due to their significant performance relative to rules-based or symbolic approaches. Nonetheless, for many tasks machine learning requires vast amounts of data and training to converge to an acceptable level of performance. A Ph.D. student from Khalifa University, Eman Hasan, is investigating another AI computation methodology called'hyperdimensional computing," which can possibly take AI systems a step closer toward human-like cognition.

The AI World Conference & Expo is packed few days with news emanating from the Expo floor, the plenary sessions, a hackathon and tracks. There's more good stuff than a writer can possibly fit into post-event coverage. Our Reporters' Notebook comprises some of the bits and pieces that we collected over the three days in Boston. In an address to attendees of AI World 2019 in Boston recently, Paul F. Nemitz, principal Advisor, Directorate General and Justice and Consumers, European Commission, issued a warning about privacy. In a talk entitled, "Democracy, Ethics and the Rule of Law in the Age of AI," Nemitz provided the European view of privacy, calling the GDPR (General Data Protection Regulation, in effect May 2018) "the most sophisticated system for protecting personal data."