Social Media and Celebrities: The Benefits of a Social Media Presence

This study analyzes celebrities and their social media presence and how they can benefit from using different social platforms. The rise of social media has caused people to rely on it for news, viewing content, and making connections with others. Celebrities no longer have to relay messages or content through their managers and publicists before releasing it. The way people interact through social media has allowed celebrities to engage their fans, enhance their career, and ultimately increase their stardom. This study investigates how social media impacts a celebrity’s career and what the best practices are for receiving positive benefits

Using reliable multicast for caching and collaboration within the world wide web

Journal ArticleThe World Wide Web has become an important medium for information dissemination. One model for synchronized information dissemination within the Web is webcasting in which data are simultaneously distributed to multiple destinations. The Web's traditional unicast client/server communication model suffers, however, when applied to webcasting; approaches that require many clients to simultaneously fetch data from the origin server using the client/server model will likely cause server and link overload. In this paper we describe a webcast design that improves upon previous designs by leveraging application level framing (ALF) design methodology. We build upon the Scalable Reliable Multicast (SRM) framework, which is based upon ALF, to create a custom protocol to meet webcast's scalability needs. We employ the protocol in an architecture consisting of two reusable components: a webcache component and a browser control component. We have implemented our design using a new SRM library called libsrm. We present the results of a simple performance evaluation and report on lessons learned while using libsrm

Soft Skills – The Missing Piece for Entrepreneurs to Grow a Business

Small businesses and entrepreneurial startups are continually cited as critical engines for economic and social growth and development of our country. Many municipalities and state governments provide tax incentives, low (or no) interest loans, infrastructure improvements and “economic gardening” – an economic development model with the fundamental idea that entrepreneurs drive economies. Economic gardening initiatives connect entrepreneurs to resources, assist with refining business models, encourage the development of essential infrastructure, assess competitive intelligence, and provide entrepreneurs with needed information. The authors suggest what is missing is assisting entrepreneurs with development of their interpersonal or soft skills

arfpy: A python package for density estimation and generative modeling with adversarial random forests

This paper introduces $\textit{arfpy}$, a python implementation of Adversarial Random Forests (ARF) (Watson et al., 2023), which is a lightweight procedure for synthesizing new data that resembles some given data. The software $\textit{arfpy}$ equips practitioners with straightforward functionalities for both density estimation and generative modeling. The method is particularly useful for tabular data and its competitive performance is demonstrated in previous literature. As a major advantage over the mostly deep learning based alternatives, $\textit{arfpy}$ combines the method's reduced requirements in tuning efforts and computational resources with a user-friendly python interface. This supplies audiences across scientific fields with software to generate data effortlessly.Comment: The software is available at https://github.com/bips-hb/arfp

Conditional Feature Importance for Mixed Data

Despite the popularity of feature importance (FI) measures in interpretable machine learning, the statistical adequacy of these methods is rarely discussed. From a statistical perspective, a major distinction is between analyzing a variable's importance before and after adjusting for covariates - i.e., between $\textit{marginal}$ and $\textit{conditional}$ measures. Our work draws attention to this rarely acknowledged, yet crucial distinction and showcases its implications. Further, we reveal that for testing conditional FI, only few methods are available and practitioners have hitherto been severely restricted in method application due to mismatching data requirements. Most real-world data exhibits complex feature dependencies and incorporates both continuous and categorical data (mixed data). Both properties are oftentimes neglected by conditional FI measures. To fill this gap, we propose to combine the conditional predictive impact (CPI) framework with sequential knockoff sampling. The CPI enables conditional FI measurement that controls for any feature dependencies by sampling valid knockoffs - hence, generating synthetic data with similar statistical properties - for the data to be analyzed. Sequential knockoffs were deliberately designed to handle mixed data and thus allow us to extend the CPI approach to such datasets. We demonstrate through numerous simulations and a real-world example that our proposed workflow controls type I error, achieves high power and is in line with results given by other conditional FI measures, whereas marginal FI metrics result in misleading interpretations. Our findings highlight the necessity of developing statistically adequate, specialized methods for mixed data

Adversarial Random Forests for Density Estimation and Generative Modeling

We propose methods for density estimation and data synthesis using a novel form of unsupervised random forests. Inspired by generative adversarial networks, we implement a recursive procedure in which trees gradually learn structural properties of the data through alternating rounds of generation and discrimination. The method is provably consistent under minimal assumptions. Unlike classic tree-based alternatives, our approach provides smooth (un)conditional densities and allows for fully synthetic data generation. We achieve comparable or superior performance to state-ofthe-art probabilistic circuits and deep learning models on various tabular data benchmarks while executing about two orders of magnitude faster on average. An accompanying R package, arf, is available on CRAN