3,458 research outputs found
Will This Video Go Viral? Explaining and Predicting the Popularity of Youtube Videos
What makes content go viral? Which videos become popular and why others
don't? Such questions have elicited significant attention from both researchers
and industry, particularly in the context of online media. A range of models
have been recently proposed to explain and predict popularity; however, there
is a short supply of practical tools, accessible for regular users, that
leverage these theoretical results. HIPie -- an interactive visualization
system -- is created to fill this gap, by enabling users to reason about the
virality and the popularity of online videos. It retrieves the metadata and the
past popularity series of Youtube videos, it employs Hawkes Intensity Process,
a state-of-the-art online popularity model for explaining and predicting video
popularity, and it presents videos comparatively in a series of interactive
plots. This system will help both content consumers and content producers in a
range of data-driven inquiries, such as to comparatively analyze videos and
channels, to explain and predict future popularity, to identify viral videos,
and to estimate response to online promotion.Comment: 4 page
Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing
Large language models have revolutionized the field of artificial
intelligence and have been used in various applications. Among these models,
ChatGPT (Chat Generative Pre-trained Transformer) has been developed by OpenAI,
it stands out as a powerful tool that has been widely adopted. ChatGPT has been
successfully applied in numerous areas, including chatbots, content generation,
language translation, personalized recommendations, and even medical diagnosis
and treatment. Its success in these applications can be attributed to its
ability to generate human-like responses, understand natural language, and
adapt to different contexts. Its versatility and accuracy make it a powerful
tool for natural language processing (NLP). However, there are also limitations
to ChatGPT, such as its tendency to produce biased responses and its potential
to perpetuate harmful language patterns. This article provides a comprehensive
overview of ChatGPT, its applications, advantages, and limitations.
Additionally, the paper emphasizes the importance of ethical considerations
when using this robust tool in real-world scenarios. Finally, This paper
contributes to ongoing discussions surrounding artificial intelligence and its
impact on vision and NLP domains by providing insights into prompt engineering
techniques
ChatGPT impacts in programming education: A recent literature overview that debates ChatGPT responses
This paper aims at a brief overview of the main impact of ChatGTP in the
scientific field of programming and learning/education in computer science. It
lists, covers and documents from the literature the major issues that have been
identified for this topic, such as applications, advantages and limitations,
ethical issues raised. Answers to the above questions were solicited from
ChatGPT itself, the responses were collected, and then the recent literature
was surveyed to determine whether or not the responses are supported. The paper
ends with a short discussion on what is expected to happen in the near future.
A future that can be extremely promising if humanity manages to have AI as a
proper ally and partner, with distinct roles and specific rules of cooperation
and interaction.Comment: 16 page
Digital television, Personal Video Recorders and Media, Automation, Data and Entertainment convergence in the home
Out of the confusion of possible delivery technologies for domestic digital video entertainment, the personal video recorder (PVR) with an electronic program guide (EPG) emerges as a key component. Serving as a content manager for video broadcasts, PVRs can automatically record, sort, schedule, store and integrate video material from different sources in a convenient, easy-to-use and timely fashion. Such devices are gradually being adopted in the homes of the developed world, and are the increasing subject of pioneering commercial enterprise, innovative experimentation and open-source community development. Going one step further, the concept of a 'MADE system' is introduced as a system with converged functionality for media, automation, data and entertainment. This article describes and compares three systems with PVR functionality and evaluates their current and future roles as a component for MADE systems. The drivers for and threats to the convergence of functionality towards a MADE system are also considered
Salience and Market-aware Skill Extraction for Job Targeting
At LinkedIn, we want to create economic opportunity for everyone in the
global workforce. To make this happen, LinkedIn offers a reactive Job Search
system, and a proactive Jobs You May Be Interested In (JYMBII) system to match
the best candidates with their dream jobs. One of the most challenging tasks
for developing these systems is to properly extract important skill entities
from job postings and then target members with matched attributes. In this
work, we show that the commonly used text-based \emph{salience and
market-agnostic} skill extraction approach is sub-optimal because it only
considers skill mention and ignores the salient level of a skill and its market
dynamics, i.e., the market supply and demand influence on the importance of
skills. To address the above drawbacks, we present \model, our deployed
\emph{salience and market-aware} skill extraction system. The proposed \model
~shows promising results in improving the online performance of job
recommendation (JYMBII) ( job apply) and skill suggestions for job
posters ( suggestion rejection rate). Lastly, we present case studies to
show interesting insights that contrast traditional skill recognition method
and the proposed \model~from occupation, industry, country, and individual
skill levels. Based on the above promising results, we deployed the \model
~online to extract job targeting skills for all M job postings served at
LinkedIn.Comment: 9 pages, to appear in KDD202
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