Figure 1: MobiV erse visualization interface: Users can observe agent behaviors in the simulation view, track individual agents, set road closures, introduce gathering events, or directly communicate with agents to influence their travel decisions and observe adaptation in real time. Abstract -- Understanding and modeling human mobility patterns is crucial for effective transportation planning and urban development. Despite significant advances in mobility research, there remains a critical gap in simulation platforms that allow for algorithm development, policy implementation, and comprehensive evaluation at scale. Traditional activity-based models require extensive data collection and manual calibration, machine learning approaches struggle with adaptation to dynamic conditions, and treding agent-based Large Language Models (LLMs) implementations face computational constraints with large-scale simulations. T o address these challenges, we propose MobiV erse, a hybrid framework leverages the efficiency of lightweight domain-specific generator for generating base activity chains with the adaptability of LLMs for context-aware modifications. A case study was conducted in Westwood, Los Angeles, where we efficiently generated and dynamically adjusted schedules for the whole population of approximately 53,000 agents on a standard PC. Our experiments demonstrate that MobiV erse successfully enables agents to respond to environmental feedback, including road closures, large gathering events like football games, and congestion, through our hybrid framework.

Hattrick is a free web-based probabilistic football manager game with over 200,000 users competing for titles at national and international levels. Launched in Sweden in 1997 as part of an MSc project, the game's slow-paced design has fostered a loyal community, with many users remaining active for decades. Hattrick's game-engine mechanics are partially hidden, and users have attempted to decode them with incremental success over the years. Rule-based, statistical and machine learning models have been developed to aid this effort and are widely used by the community. However, these models or tools have not been formally described or evaluated in the scientific literature. This study is the first to explore Hattrick using structure learning techniques and Bayesian networks, integrating both data and domain knowledge to develop models capable of explaining and simulating the game engine. We present a comprehensive analysis assessing the effectiveness of structure learning algorithms in relation to knowledge-based structures, and show that while structure learning may achieve a higher overall network fit, it does not result in more accurate predictions for selected variables of interest, when compared to knowledge-based networks that produce a lower overall network fit. Additionally, we introduce and publicly share a fully specified Bayesian network model that matches the performance of top models used by the Hattrick community. We further demonstrate how analysis extends beyond prediction by providing a visual representation of conditional dependencies, and using the best performing Bayesian network model for in-game decision-making. To support future research, we make all data, graphical structures, and models publicly available online.

Apple fans might not have to wait much longer to see the company's new flagship smartphone, the iPhone 16. The California tech giant will unveil the latest generation of iPhones at an in-person event on September 10, according to an alleged online leak. A serial Apple leaker known as Majin Bu shared a screenshot on X, formerly Twitter, which claims to shown the invite to Apple's September special event. The colour of the Apple logo in the invitation also nods to the possibility that fans might be getting a new'bronze' colour for the titanium smartphone. However, social media commenters have been sceptical of the leak's authenticity and even Majin Bu himself says: 'I have no way of verifying that this information is real, but it all seems very plausible considering the latest news.'

Public events, such as concerts and sports games, can be major attractors for large crowds, leading to irregular surges in travel demand. Accurate human mobility prediction for public events is thus crucial for event planning as well as traffic or crowd management. While rich textual descriptions about public events are commonly available from online sources, it is challenging to encode such information in statistical or machine learning models. Existing methods are generally limited in incorporating textual information, handling data sparsity, or providing rationales for their predictions. To address these challenges, we introduce a framework for human mobility prediction under public events (LLM-MPE) based on Large Language Models (LLMs), leveraging their unprecedented ability to process textual data, learn from minimal examples, and generate human-readable explanations. Specifically, LLM-MPE first transforms raw, unstructured event descriptions from online sources into a standardized format, and then segments historical mobility data into regular and event-related components. A prompting strategy is designed to direct LLMs in making and rationalizing demand predictions considering historical mobility and event features. A case study is conducted for Barclays Center in New York City, based on publicly available event information and taxi trip data. Results show that LLM-MPE surpasses traditional models, particularly on event days, with textual data significantly enhancing its accuracy. Furthermore, LLM-MPE offers interpretable insights into its predictions. Despite the great potential of LLMs, we also identify key challenges including misinformation and high costs that remain barriers to their broader adoption in large-scale human mobility analysis.

If you're wondering when Apple will hold its next event, Siri may have the answer. Ask the digital helper: "When is the next Apple event?" and it will respond with "the special event is on Tuesday, April 20, at Apple Park in Cupertino, CA. You can get all the details on Apple.com." MacRumors, which spotted the reply, says the virtual assistant is providing it in certain instances on iPhone, iPad, Mac, and HomePod. While it's an open secret that Apple is planning an event for later this month where it's expected to debut a new iPad Pro, Siri has seemingly leaked the date ahead of confirmation.

Vehicular Ad-hoc NETworks (VANET) can efficiently detect traffic congestion, but detection is not enough because congestion can be further classified as recurrent and non-recurrent congestion (NRC). In particular, NRC in an urban network is mainly caused by incidents, workzones, special events and adverse weather. We propose a framework for the real-time distributed classification of congestion into its components on a heterogeneous urban road network using VANET. We present models built on an understanding of the spatial and temporal causality measures and trained on synthetic data extended from a real case study of Cologne. Our performance evaluation shows a predictive accuracy of 87.63\% for the deterministic Classification Tree (CT), 88.83\% for the Naive Bayesian classifier (NB), 89.51\% for Random Forest (RF) and 89.17\% for the boosting technique. This framework can assist transportation agencies in reducing urban congestion by developing effective congestion mitigation strategies knowing the root causes of congestion.

Public special events, like sports games, concerts and festivals are well known to create disruptions in transportation systems, often catching the operators by surprise. Although these are usually planned well in advance, their impact is difficult to predict, even when organisers and transportation operators coordinate. The problem highly increases when several events happen concurrently. To solve these problems, costly processes, heavily reliant on manual search and personal experience, are usual practice in large cities like Singapore, London or Tokyo. This paper presents a Bayesian additive model with Gaussian process components that combines smart card records from public transport with context information about events that is continuously mined from the Web. We develop an efficient approximate inference algorithm using expectation propagation, which allows us to predict the total number of public transportation trips to the special event areas, thereby contributing to a more adaptive transportation system. Furthermore, for multiple concurrent event scenarios, the proposed algorithm is able to disaggregate gross trip counts into their most likely components related to specific events and routine behavior. Using real data from Singapore, we show that the presented model outperforms the best baseline model by up to 26% in R2 and also has explanatory power for its individual components.

Facebook is the most popular social media platform used by businesses. Facebook Pages help your brand or business promote and share its value-add and to assist in customer support. Facebook remains the primary platform for most Americans. Two-thirds of U.S. adults now report that they are Facebook users and 74 percent of Facebook users say they visit the site daily. Despite the recent criticism of Facebook's data privacy practices, both daily and monthly users are up 13 percent year-over-year.

We need to consider systems and the brain machine interaction (BMI) area in IEEE SMC cybernetics as well as include human in the loop. The purpose conference and then join the SMC society. of this article is as follows: (1) To integrate the open source II. Past held events in the world from 2008 to 2017 Facebook AI Research (FAIR) DarkForest program of Facebook with Item Response Theory (IRT), to the new open Owing to the maturity of deep learning technologies and learning system, namely, DDF learning system; (2) To integrate computer hardware, Google combined them together with DDF Go with Robot namely Robotic DDF Go system; (3) To Monte Carlo Tree to beat many top professional Go players invite the professional Go players to attend the activity to play without handicaps in 2016 and 2017 [4-5]. This year is the first Go games on site with a smart machine. The research team will year to hold Human & Smart Machines Co-Learning @ IEEE apply this technology to education, such as, playing games to SMC 2017. However, we have carried out the events of humans enhance the children concentration on learning mathematics, playing Go with the computer Go programs for almost a decade languages, and other topics. With the detected brainwaves, the [6-7]. Figure 1 shows the past held events of Human vs. Computer robot will be able to speak some words that are very much to Go Competitions from 2008 to 2017 the point for the students and to assist the teachers in classroom (https://www.youtube.com/watch?v UkSOVnbC2Y8) funded in the future.

Whenever you spot a trend plotted against time, you would be looking at a time series. The de facto choice for studying financial market performance and weather forecasts, time series are one of the most pervasive analysis techniques because of its inextricable relation to time--we are always interested to foretell the future. One intuitive way to make forecasts would be to refer to recent time points. Today's stock prices would likely be more similar to yesterday's prices than those from five years ago. Hence, we would give more weight to recent than to older prices in predicting today's price.