Figure 1: The test reward value of SG-SPEN's outputs trained in the supervised setting and semi-supervised settings We appreciate the reviewers' comments and concerns. SG-SPEN achieves 91.0% and DVN achieves 90.5% token-level accuracy. SG-SPEN with domain-knowledge based citation reward function, which resulted in 90.6% token-level accuracy. PG training with EMA baseline when the model is pre-trained with the labeled data. GE based on Mann & McCallum (2010).

Graphical user interface (GUI) agents have advanced rapidly but still struggle with complex tasks involving novel UI elements, long-horizon actions, and personalized trajectories. In this work, we introduce Instruction Agent, a GUI agent that leverages expert demonstrations to solve such tasks, enabling completion of otherwise difficult workflows. Given a single demonstration, the agent extracts step-by-step instructions and executes them by strictly following the trajectory intended by the user, which avoids making mistakes during execution. The agent leverages the verifier and backtracker modules further to improve robustness. Both modules are critical to understand the current outcome from each action and handle unexpected interruptions(such as pop-up windows) during execution. Our experiments show that Instruction Agent achieves a 60% success rate on a set of tasks in OSWorld that all top-ranked agents failed to complete. The Instruction Agent offers a practical and extensible framework, bridging the gap between current GUI agents and reliable real-world GUI task automation.

These authors contributed equally to this work *Corresponding author. Abstract: Social media platforms can quickly disseminate STEM content to diverse audiences, but their operation can be mysterious. We used open-source machine learning methods such as clustering, regression, and sentiment analysis to analyze over 1000 videos and metrics thereof from 6 social media STEM creators. Our data provide insights into how audiences generate interest signals(likes, bookmarks, comments, shares), on the correlation of various signals with views, and suggest that content from newer creators is disseminated differently. We also share insights on how to optimize dissemination by analyzing data available exclusively to content creators as well as via sentiment analysis of comments. Introduction: Social media platforms such as Instagram, TikTok, and YouTube provide a new venue to promote STEM education, inspire the next generation of diverse scientists, and share knowledge to lower barriers to academia(1-3). Unlike many existing venues, social media is broadly accessible and not limited to those with significant resources devoted to their education. Content can be quickly disseminated to large diverse audiences of all ages and backgrounds(4).

Cultural evolution is driven by how we choose what to consume and share with others. A common belief is that the cultural artifacts that succeed are ones that balance novelty and conventionality. This balance theory suggests that people prefer works that are familiar, but not so familiar as to be boring; novel, but not so novel as to violate the expectations of their genre. We test this idea using a large dataset of fanfiction. We apply a multiple regression model and a generalized additive model to examine how the recognition a work receives varies with its novelty, estimated through a Latent Dirichlet Allocation topic model, in the context of existing works. We find the opposite pattern of what the balance theory predicts$\unicode{x2014}$overall success decline almost monotonically with novelty and exhibits a U-shaped, instead of an inverse U-shaped, curve. This puzzle is resolved by teasing out two competing forces: sameness attracts the mass whereas novelty provides enjoyment. Taken together, even though the balance theory holds in terms of expressed enjoyment, the overall success can show the opposite pattern due to the dominant role of sameness to attract the audience. Under these two forces, cultural evolution may have to work against inertia$\unicode{x2014}$the appetite for consuming the familiar$\unicode{x2014}$and may resemble a punctuated equilibrium, marked by occasional leaps.

Menu system design is a challenging task involving many design options and various human factors. For example, one crucial factor that designers need to consider is the semantic and systematic relation of menu commands. However, capturing these relations can be challenging due to limited available resources. With the advancement of neural language models, large language models can utilize their vast pre-existing knowledge in designing and refining menu systems. In this paper, we propose MenuCraft, an AI-assisted designer for menu design that enables collaboration between the designer and a dialogue system to design menus. MenuCraft offers an interactive language-based menu design tool that simplifies the menu design process and enables easy customization of design options. MenuCraft supports a variety of interactions through dialog that allows performing zero/few-shot learning.

Do you want to learn natural language processing and looking for Best Resources to Learn Natural Language Processing?… If yes, then you are in the right place. In this article, I have listed all the best resources to learn natural language processing including Online Courses, Tutorials, Books, and YouTube Videos. So, give your few minutes and find out the best resources to learn natural language processing. You can bookmark this article so that you can refer to this article later.

GitHub is a goldmine of free resources. But with so much information, it's hard to know what to prioritize. Bookmark these 10 repositories to guarantee you learn from the best. Start with a strong base in Python and related libraries, then work your way through each relevant application of ML and DL. Jeande's work is based on his own experience and is crafted for users at a range of experience levels.

In the beginning website, design developers and designers designed websites using HTML. Soon, the internet was formless and empty, darkness was over the surface of the deep web, and the Spirit of Code was hovering over the pinnacle of utmost ignorance. We've come a long way from that time. The internet is still a dark, dreadful place, but it's much more stylish, sophisticated, and amazing now. Website Design has grown exponentially in scale and sophistication over the last few years, thanks to new Artificial Intelligence-based website creation tools that are dominating the digital marketing industry.

The Industrial IoT (IIoT) eco-system continues to recognize the benefits of artificial intelligence (AI) and as a result, we spend large sums of money in AI work where we may be disappointed with the results. To be successful, we have learned that deploying AI in an IIoT applications needs to be an evolution and not a revolution especially when used in a Operational Technology (OT) environment. Today's talk will share our experiences in developing AI based solutions including successes and challenges and then a recommendation how to evolve your IIoT solutions with an AI component. Using AI in IIoT may yield incredible results; however, the right expectations need to be set from the beginning as AI requires new technologies and skills to yield those results. Our objective is to provide insights in leveraging an AI maturity model and methodologies so you can evolve your IIoT solutions over time as real world experience is gained.