Analogy-Based Estimation (ABE) is a popular method for non-algorithmic estimation due to its simplicity and effectiveness. The Analogy-Based Estimation (ABE) model was proposed by researchers, however, no optimal approach for reliable estimation was developed. Achieving high accuracy in the ABE might be challenging for new software projects that differ from previous initiatives. This study (conducted in June 2024) proposes a Firefly Algorithm-guided Analogy-Based Estimation (FAABE) model that combines FA with ABE to improve estimation accuracy. The FAABE model was tested on five publicly accessible datasets: Cocomo81, Desharnais, China, Albrecht, Kemerer and Maxwell. To improve prediction efficiency, feature selection was used. The results were measured using a variety of evaluation metrics; various error measures include MMRE, MAE, MSE, and RMSE. Compared to conventional models, the experimental results show notable increases in prediction precision, demonstrating the efficacy of the Firefly-Analogy ensemble.

Adobe Firefly is a deceptively powerful AI playground to generate images, videos, and more. Here's how to make the most of it. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Adobe Firefly feels like the best-kept secret in software right now.

When you purchase through links in our articles, we may earn a small commission. Adobe unveiled new AI additions to its Firefly image generation tool, plus Photoshop, Premiere, and Lightroom. First, there was generative AI, allowing creators, editors and memelords to create artificial worlds with just a few words. Now, Adobe is offering the ability to edit those worlds with Prompt to Edit, a new feature within Firefly, plus audio capabilities. Adobe announced the new capabilities at its MAX conference, where it typically rolls out new capabilities within its Creative Cloud suite as well as Firefly, its AI image generator -- which now includes soundtracks and AI voiceovers.

At Adobe's annual MAX conference, the company also teased a ChatGPT integration and a new AI assistant in Photoshop. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Adobe is leaning heavily into artificial intelligence. At the company's annual MAX conference in Los Angeles, it announced a slew of new features for its creative apps, almost all of which include some kind of new AI capability.

We propose injecting notions of fairness into multi-robot motion planning. When robots have competing interests, it is important to optimize for some kind of fairness in their usage of resources. In this work, we explore how the robots' energy expenditures might be fairly distributed among them, while maintaining mission success. We formulate a distributed fair motion planner and integrate it with safe controllers in a algorithm called FiReFly. For simulated reach-avoid missions, FiReFly produces fairer trajectories and improves mission success rates over a non-fair planner. We find that real-time performance is achievable up to 15 UAVs, and that scaling up to 50 UAVs is possible with trade-offs between runtime and fairness improvements.

We sincerely thank all reviewers for their valuable comments and we address individual questions below. R1: This paper only compares to the 2 years ago's work (DARTS). R2: ENAS/DARTS are fairly different architecture search algorithms. R2: It would be nice to have an explicit related work section. We will move the related work to main content.

Current diversification strategies for text-to-image (T2I) models often ignore contextual appropriateness, leading to over-diversification where demographic attributes are modified even when explicitly specified in prompts. This paper introduces DIVBENCH, a benchmark and evaluation framework for measuring both under- and over-diversification in T2I generation. Through systematic evaluation of state-of-the-art T2I models, we find that while most models exhibit limited diversity, many diversification approaches overcorrect by inappropriately altering contextually-specified attributes. We demonstrate that context-aware methods, particularly LLM-guided FairDiffusion and prompt rewriting, can already effectively address under-diversity while avoiding over-diversification, achieving a better balance between representation and semantic fidelity.

This paper addresses the challenges faced by algorithms, such as the Firefly Algorithm (FA) and the Genetic Algorithm (GA), in constrained optimization problems. While both algorithms perform well for unconstrained problems, their effectiveness diminishes when constraints are introduced due to limitations in exploration, exploitation, and constraint handling. To overcome these challenges, a hybrid FAGA algorithm is proposed, combining the strengths of both algorithms. The hybrid algorithm is validated by solving unconstrained benchmark functions and constrained optimization problems, including design engineering problems and combinatorial problems such as the 0-1 Knapsack Problem. The proposed algorithm delivers improved solution accuracy and computational efficiency compared to conventional optimization algorithm. This paper outlines the development and structure of the hybrid algorithm and demonstrates its effectiveness in handling complex optimization problems.

Adobe is now adding its AI-based video generator, Firefly, to its video editing software Premiere Pro. The Firefly model can be used to extend a video clip or generate video from still images or text instructions. This was first brought to our attention by The Verge. The Generative Extend tool will initially be available in beta and can extend the length of a video clip by up to two seconds with an image resolution of 720p or 1080p and a refresh rate of 24 frames-per-second. This tool will also be applicable to ambient sounds and sound effects, but not to music or speech.