Multi-perspective datasets that combine firstperson and third-person views are rare and typically include only a Egocentric video has seen increased interest in recent years, as limited number of activities and do not last long enough to capture it is used in a range of areas. However, most existing datasets the full range of interactions and social dynamics characteristic of are limited to a single perspective. In this paper, we present the everyday life. CASTLE 2024 dataset, a multimodal collection containing ego-and In this paper, we introduce the CASTLE 2024 dataset, a multimodal exo-centric (i.e., first-and third-person perspective) video and audio multi-perspective collection of ego-centric (first-person) from 15 time-aligned sources, as well as other sensor streams and and exo-centric (third-person) high-resolution video recordings, auxiliary data. The dataset was recorded by volunteer participants augmented with additional sensor streams, designed to capture the over four days in a fixed location and includes the point of view complexity of daily human experiences. The dataset captures the of 10 participants, with an additional 5 fixed cameras providing an experience and daily interaction of ten volunteer participants over exocentric perspective. The entire dataset contains over 600 hours the course of four days. It shows a broad range of domestic and of UHD video recorded at 50 frames per second. In contrast to other social activities, including cooking, eating, cleaning, meeting and datasets, CASTLE 2024 does not contain any partial censoring, such leisure activities, capturing authentic interactions among participants.

Gardening experts Mickey and Vicky Popat join'Fox & Friends Weekend' to celebrate National Gardening Week. Gardening could help aging adults stay sharp later in life, according to a recent study published in the Journal of Environmental Psychology. Researchers at the University of Edinburgh in Scotland found that tending to gardens at an older age is associated with "small but detectable cognitive benefits." The long-term study tracked participants who shared details of their lifestyles and completed "frequent assessments" of their thinking skills up to age 90. ELLEN DEGENERES HAS OSTEOPOROSIS: HERE'S WHAT TO KNOW ABOUT THE PAINFUL BONE CONDITION The "Lothian Birth Cohort 1921" study followed people who were born in the Edinburgh area, starting at age 11.

The importance of effective detection is underscored by the fact that socialbots imitate human behavior to propagate misinformation, leading to an ongoing competition between socialbots and detectors. Despite the rapid advancement of reactive detectors, the exploration of adversarial socialbot modeling remains incomplete, significantly hindering the development of proactive detectors. To address this issue, we propose a mathematical Structural Information principles-based Adversarial Socialbots Modeling framework, namely SIASM, to enable more accurate and effective modeling of adversarial behaviors. First, a heterogeneous graph is presented to integrate various users and rich activities in the original social network and measure its dynamic uncertainty as structural entropy. By minimizing the high-dimensional structural entropy, a hierarchical community structure of the social network is generated and referred to as the optimal encoding tree. Secondly, a novel method is designed to quantify influence by utilizing the assigned structural entropy, which helps reduce the computational cost of SIASM by filtering out uninfluential users. Besides, a new conditional structural entropy is defined between the socialbot and other users to guide the follower selection for network influence maximization. Extensive and comparative experiments on both homogeneous and heterogeneous social networks demonstrate that, compared with state-of-the-art baselines, the proposed SIASM framework yields substantial performance improvements in terms of network influence (up to 16.32%) and sustainable stealthiness (up to 16.29%) when evaluated against a robust detector with 90% accuracy.

Text description provided by the architects. In Yangliu Park near the AI technology city of Xiangjiang New District is the Xiangjiang Artificial Intelligence Technology Valley. As the parlor serving the city, the valley offers indoor and outdoor exhibitions, meetings, exchanges, receptions, and multiple services. Here we can learn and explore future frontier technology. In a picturesque park, this is also a nice place for leisure activities.

Accurate forecasting of medical service requirements is an important big data problem that is crucial for resource management in critical times such as natural disasters and pandemics. With the global spread of coronavirus disease 2019 (COVID-19), several concerns have been raised regarding the ability of medical systems to handle sudden changes in the daily routines of healthcare providers. One significant problem is the management of ambulance dispatch and control during a pandemic. To help address this problem, we first analyze ambulance dispatch data records from April 2014 to August 2020 for Nagoya City, Japan. Significant changes were observed in the data during the pandemic, including the state of emergency (SoE) declared across Japan. In this study, we propose a deep learning framework based on recurrent neural networks to estimate the number of emergency ambulance dispatches (EADs) during a SoE. The fusion of data includes environmental factors, the localization data of mobile phone users, and the past history of EADs, thereby providing a general framework for knowledge discovery and better resource management. The results indicate that the proposed blend of training data can be used efficiently in a real-world estimation of EAD requirements during periods of high uncertainties such as pandemics.

School district Superintendents Dan Stepenosky and Art Javis weigh in on reopening schools amid the coronavirus pandemic. As COVID-19 cases surge across the country, millions of students are once again shifting to all-remote learning. Between Sunday, Nov. 22 and Monday, Nov. 23, the percentage of students exclusively attending school online jumped from 36.9 to 40 percent. Once again, school leaders and government officials are scrambling to figure out logistics. But there's a huge remote learning side effect they've yet to consider: Student loneliness.

An evening meal and drinks out can help you sleep better and feel more refreshed, but going for brunch has the opposite effect and makes you more tired, study finds. Researchers from the Karolinska Institute, Sweden, studied the behaviour and sleepiness of 641 working adults in Sweden, particularly on weekends and days off. Sleepiness can impair cognitive ability, motivation, and behavior including on our desire to socialise with others, according to the team led by Benjamin Holding. The relationship between social activity and subsequent sleepiness and sleep duration was complex and depended on the time of day, the team found. Going out for brunch can make you more sleepy in the afternoon and sleep less at night, going out for dinner can make you more refreshed and sleep better.

Health professionals have spoken out about the impact playing Fortnite has on children - likening its addictive nature to that of hard drugs. Played by more than 200 million users, the cartoon multiplayer shooter game is costing children sleep, their school work and causing them to become violent. The latest instalment of the popular video game Fortnite: Battle Royale is seeing children drop out of social activities like playing sport. Behavioural specialists say that some children are even battling gaming addiction as a result from constantly playing the game. Health professionals have spoken out about the impact playing Fortnite has on children likening it to them being addicted to drugs.

User engagement in online social networking depends critically on the level of social activity in the corresponding platform--the number of online actions, such as posts, shares or replies, taken by their users. Can we design data-driven algorithms to increase social activity? At a user level, such algorithms may increase activity by helping users decide when to take an action to be more likely to be noticed by their peers. At a network level, they may increase activity by incentivizing a few influential users to take more actions, which in turn will trigger additional actions by other users. In this paper, we model social activity using the framework of marked temporal point processes, derive an alternate representation of these processes using stochastic differential equations (SDEs) with jumps and, exploiting this alternate representation, develop two efficient online algorithms with provable guarantees to steer social activity both at a user and at a network level. In doing so, we establish a previously unexplored connection between optimal control of jump SDEs and doubly stochastic marked temporal point processes, which is of independent interest. Finally, we experiment both with synthetic and real data gathered from Twitter and show that our algorithms consistently steer social activity more effectively than the state of the art.

Link prediction in social networks is to infer the new links likely to be formed next or to reconstruct the links that are currently missing. Other than the pure topological network structures, social networks are often associated with rich information of social activities of users, such as tweeting, retweeting, and replying. Social theories such as social influence indicate that social activities could have potential impacts on the neighbors, and links in social media could be the results of the social influence among users. It motivates us to learn and model social influence among users to tackle the link prediction problem. However, this is a non-trivial task since it is challenging to model heterogeneous social activities. Traditional methods often define universal metrics of social influence for all users, but even for the same activity of a user, the influence towards different neighbors might not be the same. It motivates a personalized learning schema. In information theory, if a time-series signal influences another, then the uncertainty in the latter one will be reduced, given the distribution of the former one. Thus, we are motivated to learn social influence based on the timestamps of social activities. Given the timestamps of each user, we use entropy to measure the reduction of uncertainty of his/her neighbors. The learned social influence is then incorporated into a graph based link prediction model to perform joint learning. Through comprehensive experiments, we demonstrate that the proposed framework can perform better than the state-of-the-art methods on different real-world networks.