Unmanned aerial vehicles (UAVs) are becoming more commonly used in populated areas, raising concerns about noise pollution generated from their propellers. This study investigates the acoustic performance of unconventional propeller designs, specifically toroidal and uneven-blade spaced propellers, for their potential in reducing psychoacoustic annoyance. Our experimental results show that these designs noticeably reduced acoustic characteristics associated with noise annoyance.

The lack of contextual information in text data can make the annotation process of text-based emotion classification datasets challenging. As a result, such datasets often contain labels that fail to consider all the relevant emotions in the vocabulary. This misalignment between text inputs and labels can degrade the performance of machine learning models trained on top of them. As re-annotating entire datasets is a costly and time-consuming task that cannot be done at scale, we propose to use the expressive capabilities of large language models to synthesize additional context for input text to increase its alignment with the annotated emotional labels. In this work, we propose a formal definition of textual context to motivate a prompting strategy to enhance such contextual information. We provide both human and empirical evaluation to demonstrate the efficacy of the enhanced context. Our method improves alignment between inputs and their human-annotated labels from both an empirical and human-evaluated standpoint.

In a conversational dialogue, speakers may have different emotional states and their dynamics play an important role in understanding dialogue's emotional discourse. However, simply detecting emotions is not sufficient to entirely comprehend the speaker-specific changes in emotion that occur during a conversation. To understand the emotional dynamics of speakers in an efficient manner, it is imperative to identify the rationale or instigator behind any changes or flips in emotion expressed by the speaker. In this paper, we explore the task called Instigator based Emotion Flip Reasoning (EFR), which aims to identify the instigator behind a speaker's emotion flip within a conversation. For example, an emotion flip from joy to anger could be caused by an instigator like threat. To facilitate this task, we present MELD-I, a dataset that includes ground-truth EFR instigator labels, which are in line with emotional psychology. To evaluate the dataset, we propose a novel neural architecture called TGIF, which leverages Transformer encoders and stacked GRUs to capture the dialogue context, speaker dynamics, and emotion sequence in a conversation. Our evaluation demonstrates state-of-the-art performance (+4-12% increase in F1-score) against five baselines used for the task. Further, we establish the generalizability of TGIF on an unseen dataset in a zero-shot setting. Additionally, we provide a detailed analysis of the competing models, highlighting the advantages and limitations of our neural architecture.

The Bureau of Multiversal Arbitration is an unusual workplace. Maude Fletcher's alright, though she needs to learn how to turn off caps lock in the company chat. But trying to deal with Byron G Snodgrass is like handling an energetic poodle, and Phil is a bit stiff. Byron G Snodgrass is an energetic poodle. A peace lily, I think.

This article was published as a part of the Data Science Blogathon. This article will solve a famous interview question that the greedy approach can optimally solve. You can find the complete question here. I will teach everything from very basic, like explaining the algorithm, proof of concept, and time complexity, and then I will show you the complete code. This approach solves the problem by selecting the best optimum possibility available.

"Science, my lad, is made up of mistakes, but they are mistakes which it is useful to make, because they lead little by little to the truth," wrote Jules Verne. I've been in the field of automation long enough to see that many of the most impressive advancements are still quite new. Take driverless cars, for instance. A decade ago, they were little more than a fantasy, but in February 2019, Elon Musk predicted that we'd have the technology for a fully self-driving car by the end of this year. Driverless cars promise to transform transportation, but something like machine learning promises to transform everything.

Neighbor-ly is a fictional smart home object designed specifically to be in urban homes. It's designed to address a problem that is all too common for people sharing walls with neighbors in city apartments, dealing with those obscure and obnoxious sounds that travel to your home in the most inconvenient of times. Is that someone on stilts walking around upstairs? Are they moving and dragging furniture all afternoon? Are those bags of marbles being dropped on the floor?

The real opportunity for businesses is to blend AI with human effort,

As little as five years ago, most workplaces and office spaces were still stuck in the past, all but using Spinning Jenny's to get things done. But fast forward to now and buzzwords like virtual spaces, voice control and virtual reality technologies are no longer things you'd find in the Minority Report but everyday additions to the modern world. The era of disruptive technology is here. But we are less focused on the technology that is coming into play and more obsessed with what this tech will do to our working routines. Of course, being able to predict the future is not possible (if it was we would be winning that billion dollar lotto without a shadow of a doubt). But even though it is impossible to be totally accurate, that doesn't mean we can't foretell how certain trends will influence the future, especially given some of the tech advancements won't need any transition time, such as augmented reality, voice control and iris scanners.

When Apple released its software application, Siri, in 2011, iPhone users had high expectations for their intelligent personal assistants. Yet despite its impressive and growing capabilities, Siri often makes mistakes. The software's imperfections highlight the clear limitations of current AI: today's machine intelligence can't understand the varied and changing needs and preferences of human life. However, as artificial intelligence advances, experts believe that intelligent machines will eventually – and probably soon – understand the world better than humans. While it might be easy to understand how or why Siri makes a mistake, figuring out why a superintelligent AI made the decision it did will be much more challenging.