Smart LED light bulbs are one of the easiest ways to get into the IoT space. These smart lighting solutions let you control your home's illumination from your phone and other connected devices, and in addition to that practicality, they also inject some fun into your space. Color-changing bulbs have a plethora of RGB options for you to customize the lighting mood for your next movie night, date night or game day, or you can opt for cozy warm white light when you need to unwind at the end of a long day. It goes without saying that many of these smart LED light bulbs work with Amazon's Alexa and the Google Assistant, so if you already have a smart home setup in the works, you can find one that fits into your chosen ecosystem. And arguably the best thing about these devices is that they can fit into any budget; affordable and advanced options have flooded the space over the past few years. We've tested out a bunch of smart lights over the years, and these are our current favorites. If you've done any research into smart lights, you've probably come across Philips Hue bulbs.

Analyzing user reviews for sentiment towards app features can provide valuable insights into users' perceptions of app functionality and their evolving needs. Given the volume of user reviews received daily, an automated mechanism to generate feature-level sentiment summaries of user reviews is needed. Recent advances in Large Language Models (LLMs) such as ChatGPT have shown impressive performance on several new tasks without updating the model's parameters i.e. using zero or a few labeled examples. Despite these advancements, LLMs' capabilities to perform feature-specific sentiment analysis of user reviews remain unexplored. This study compares the performance of state-of-the-art LLMs, including GPT-4, ChatGPT, and LLama-2-chat variants, for extracting app features and associated sentiments under 0-shot, 1-shot, and 5-shot scenarios. Results indicate the best-performing GPT-4 model outperforms rule-based approaches by 23.6% in f1-score with zero-shot feature extraction; 5-shot further improving it by 6%. GPT-4 achieves a 74% f1-score for predicting positive sentiment towards correctly predicted app features, with 5-shot enhancing it by 7%. Our study suggests that LLM models are promising for generating feature-specific sentiment summaries of user reviews.

With thousands of potential matches, opening up any dating app can feel like wading through a sea of spam and unwanted texts. But now, Hinge has made it easier than ever to avoid time wasters and toxicity by letting users filter out unwanted terms. The new Hidden Words tool automatically blocks'Likes with Comments' containing words, phrases or even emojis, as chosen by the users. And from'Sunday Roast' to'F1', Hinge users have taken to social media to share the phrases and dating clichés that they're sick of hearing about. One X, formerly Twitter user, wrote: 'Finally, a dating app feature I can get behind.'

Apple's app platform is finally opening up a bit. Today, the company said that it will allow developers to utilize new in-app experiences, including streaming games, accessing mini-apps, and talking with chatbots. That means devs can create a single app that houses an easily accessible catalog of their streaming titles. Perhaps we'll finally see a usable Game Pass app from Microsoft (or even its long-awaited mobile game store). The new in-app experiences, which also includes things like mini-games and plug-ins, will also get new discovery opportunities.

This paper presents a novel framework that utilizes Natural Language Processing (NLP) techniques to understand user feedback on mobile applications. The framework allows software companies to drive their technology value stream based on user reviews, which can highlight areas for improvement. The framework is analyzed in depth, and its modules are evaluated for their effectiveness. The proposed approach is demonstrated to be effective through an analysis of reviews for sixteen popular Android Play Store applications over a long period of time.

Annotation guidelines used to guide the annotation of training and evaluation datasets can have a considerable impact on the quality of machine learning models. In this study, we explore the effects of annotation guidelines on the quality of app feature extraction models. As a main result, we propose several changes to the existing annotation guidelines with a goal of making the extracted app features more useful and informative to the app developers. We test the proposed changes via simulating the application of the new annotation guidelines and then evaluating the performance of the supervised machine learning models trained on datasets annotated with initial and simulated guidelines. While the overall performance of automatic app feature extraction remains the same as compared to the model trained on the dataset with initial annotations, the features extracted by the model trained on the dataset with simulated new annotations are less noisy and more informative to the app developers. Secondly, we are interested in what kind of annotated training data is necessary for training an automatic app feature extraction model. In particular, we explore whether the training set should contain annotated app reviews from those apps/app categories on which the model is subsequently planned to be applied, or is it sufficient to have annotated app reviews from any app available for training, even when these apps are from very different categories compared to the test app. Our experiments show that having annotated training reviews from the test app is not necessary although including them into training set helps to improve recall. Furthermore, we test whether augmenting the training set with annotated product reviews helps to improve the performance of app feature extraction. We find that the models trained on augmented training set lead to improved recall but at the cost of the drop in precision.

It could mean the end of trips to Ikea, and allow you to transform your living room at the touch of a button. Dubbed the'digitally transformable sofa,' Lift-Bit is a series of hexagonal stools that fit together honeycomb-style. Powered through a tablet app and simple hand gestures, the stools shift their height around in a matter of seconds to transform into whatever piece of furniture you need. Dubbed the'digitally transformable sofa,' Lift-bit is a series of hexagonal stools that fit together honeycomb-style and allows you to create a number of combinations. Dubbed the'digitally transformable sofa,' Lift-Bit is a series of hexagonal stools that fit together honeycomb-style and allows you to create a number of combinations.