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In this post, I demonstrate how you can extend the classic implementation of the Floyd-Warshall algorithm with route tracking capability to reconstruct the shortest paths routes later.

Kivach is an open-source decentralized application (Dapp) for cascading donations to GitHub repositories. Etherpad is a real-time collaborative text editor launched in November 2008 by David Greenspan, Aaron Iba, and J.D. Zamfirescu. Flarum is a lightweight forum platform that users can easily extend and modify to suit their needs.

Ethereum is a decentralized platform that empowers developers to create applications using smart contracts, enabling trustless transactions without intermediaries. It operates on a global network, making it secure and transparent. This article presents Ethereum's core concepts in five levels: a simple explanation for children, a relatable overview for teens, a more technical description for college students, an advanced understanding for graduate students, and an...

Coze encourages experimentation and innovation in AI chatbot development. This leads to the creation of new and exciting chatbot applications.

Powerledger (POWR) has officially completed its integration with the Solana ecosystem. POWR is set to play a pivotal role in enabling the tokenisation and trading of renewable energy assets. This integration enables Powerledger’s platform to scale faster, support high-volume energy and environmental commodities transactions.

Noisefit pro 4 is a color smartwatch with a built-in pedometer. The code is dynamic and it can connect to any noisefit watch nearby. I’m not going to share the complete code at the moment but below is a sneak peek. I really hate sharing my personal data with any company.

HackerNoon's Startups of The Year is back for its 3rd season. With nominations now open, see how 2023 nominees gathered their community's favor and votes to get closer to the win.

How are you, hacker? 🪐Want to know what's trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. How Fortnite Creative and UEFN Is The Next Big Creative Moneymaker and Why By @goldiechan [ 6 Min read ] Fortnite Creative ...

AI-generated images are evaluated using both human judgment (on alignment, photorealism, aesthetics, and originality) and automated metrics like CLIPScore, FID, and fairness measures. Crowdsourcing ensures reliability, while machine metrics offer precision.

AI image generation models are tested with diverse datasets like MS-COCO, DrawBench, and custom art or fairness benchmarks. These scenarios examine everything from image quality to fairness, cognitive skills, and aesthetic creativity.

The HEIM benchmark is designed to evaluate text-to-image models across 12 aspects, including alignment, quality, bias, toxicity, and efficiency, offering a holistic assessment for real-world deployment.

Researchers at Stanford University, Microsoft, Adobe and Aleph Alpha have created a framework for analyzing scenarios and metrics. They have also created a Datasheet for human evaluation procedures. The paper is available online at: http://hackernoon.com/preview/2fOYKcoYOp4SGP4hkuLl.

This study identifies 12 key aspects for evaluating text-to-image models but acknowledges several limitations. It highlights potential gaps in considering other dimensions, limitations in the current metrics for bias and efficiency, and challenges posed by subjective human evaluation, particularly in aesthetics and originality. Future research is encouraged to explore additional aspects and refine the metrics for a more comprehensive evaluation of image generation models.

HEIM introduces a new benchmark for evaluating text-to-image models across 12 critical aspects, from alignment to robustness. By analyzing 26 recent models, the findings highlight how different models perform in various aspects, emphasizing the need for future research on creating models that excel across multiple areas. The evaluation pipeline, images, and human evaluation results are shared to foster transparency and reproducibility, encouraging the community...

This section discusses the importance of holistic benchmarking in AI, particularly for image generation models. Existing benchmarks have largely focused on image quality and alignment, relying mainly on automated metrics. While recent efforts have incorporated human evaluation, they often fall short of assessing a wider range of aspects, including aesthetics, originality, reasoning, and ethical implications. Our work addresses these gaps by implementing a comprehensive...

This study evaluates 26 text-to-image models across 12 key aspects using 62 scenarios and 25 metrics. DALL-E 2 leads in text-image alignment, while Dreamlike Photoreal excels in photorealism. Despite their strengths, most models fall short in reasoning, photorealism, and multilinguality, with bias and toxicity remaining significant concerns. Models like minDALL-E and SafeStableDiffusion mitigate bias well, while Dreamlike Diffusion and Openjourney shine in originality and...

We evaluate 26 recent text-to-image models, spanning diffusion, autoregressive, and GAN types, with sizes from 0.4B to 13B parameters. The models are compared based on their organizations, accessibility (open or closed), and default inference configurations from APIs, GitHub, or Hugging Face repositories. Table 4 summarizes key model properties for a clear comparison.

HEIM introduces a diverse set of metrics, combining human and automated evaluations to assess text-to-image models across 12 aspects. Human metrics help capture nuanced qualities like photorealism, originality, and alignment, while new metrics close gaps for fairness, robustness, multilinguality, and efficiency. Crowdsourcing ensures realistic and representative evaluations.

To evaluate 12 key aspects of text-to-image models, HEIM curates 62 practical scenarios. These include established ones like MS-COCO and new ones for originality, aesthetics, bias, and fairness. The scenarios provide robust tests for aspects such as toxicity, quality, and creativity, offering comprehensive model assessment across varied tasks.

HEIM evaluates text-to-image models across 12 critical aspects, including text-image alignment, aesthetics, originality, bias, and multilinguality. By using diverse scenarios and metrics, including human and automated evaluations, it provides a comprehensive understanding of each model’s performance and ethical implications.

HEIM evaluates text-to-image models through four core components: aspect, scenario, adaptation, and metric. These components capture a model's performance across diverse tasks using a combination of human and automated metrics. Key adaptations include zero-shot prompting and prompt engineering.

The Holistic Evaluation of Text-to-Image Models (HEIM) benchmark assesses 26 models across 12 aspects, revealing diverse strengths but no all-around leader. The benchmark evaluates text-image alignment, bias, aesthetics, and more, using human and automated metrics

Quantity and variety of data fuel the rise of complex and sophisticated ML algorithms to handle AI workloads.

Round 2 of the #bitcoin Writing Contest has concluded. Check out the top 20 entries, including our top 3 winners and bonus prize recipients. Don't miss your chance to join Round 3, running from October 1 to November 30, 2024. Visit contests.hackernoon.com for more details and upcoming opportunities!