Text-to-image models have made significant strides, producing impressive results in generating images from textual descriptions. However, creating a scalable pipeline for deploying these models in production remains a challenge. Achieving the right balance between automation and human feedback is critical to maintain both scale and quality. While automation can handle large volumes, human oversight is still an essential component to ensure that the generated images meet the desired standards and are aligned with the creative vision. This paper presents a new pipeline that offers a fully automated, scalable solution for generating marketing images of commercial products using text-to-image models. The proposed system maintains the quality and fidelity of images, while also introducing sufficient creative variation to adhere to marketing guidelines. By streamlining this process, we ensure a seamless blend of efficiency and human oversight, achieving a 30.77% increase in marketing object fidelity using DINOV2 and a 52.00% increase in human preference over the generated outcome.

Key management has long remained a difficult unsolved problem in the field of usable security. While password-based key derivation functions (PBKDFs) are widely used to solve this problem in centralized applications, their low entropy and lack of a recovery mechanism make them unsuitable for use in decentralized contexts. The multi-factor key derivation function (MFKDF) is a recently proposed cryptographic primitive that aims to address these deficiencies by incorporating commonly used authentication factors into the key derivation process. In this paper, we implement an MFKDF-based Ethereum wallet and perform a user study with 27 participants to directly compare its usability against traditional cryptocurrency wallet architectures. Our results show that MFKDF-based applications outperform conventional key management approaches on both subjective and objective metrics, with a 37% higher average SUS score (p < 0.0001) and 71% faster task completion times (p < 0.0001) for the MFKDF-based wallet.

Decentralized Autonomous Organizations (DAOs) have emerged as a novel way to coordinate a group of (pseudonymous) entities toward a shared vision (e.g., promoting sustainability). In just a few years, over 4,000 DAOs have been launched in various domains, such as investment, education, health, and research. Despite such rapid growth and diversity, it is unclear how these DAOs actually work in practice. Given this, we aim to unpack how (well) DAOs work in practice. We conducted an in-depth analysis of a diverse set of 10 DAOs of various categories and smart contracts, leveraging on-chain data and interviewing DAO members. Specifically, we define metrics to characterize key aspects of DAOs, such as the degrees of decentralization and autonomy. We observed some DAOs having poor decentralization in voting, while decentralization has improved over time for one-person-one-vote DAOs. Lastly, we offer a set of design implications for future DAOs based on our findings.

Recommender systems commonly train on user engagements because of their abundance, immediacy of feedback, and the insights they provide into users preferences. However, this approach may unintentionally prioritize optimizing short-term engagements over a product’s or business’s long-term objectives. At Netflix, our recommender systems are designed with the goal of maximizing long-term member satisfaction. To achieve this objective, we adopt a practical approach that augments engagement data with reward signals aligned with long term member satisfaction. This process of identifying, evaluating, and integrating reward signals into an existing learning algorithm is what we term reward innovation. In this work, we present the challenges of applying this approach to a large-scale recommender system and share our approach to addressing them.

Crash localization, an important step in debugging crashes, is challenging when dealing with an extremely large number of diverse applications and platforms and underlying root causes. Large-scale error reporting systems, e.g., Windows Error Reporting (WER), commonly rely on manually developed rules and heuristics to localize blamed frames causing the crashes. As new applications and features are routinely introduced and existing applications are run under new environments, developing new rules and maintaining existing ones become extremely challenging.

We propose a fully automated system that simultaneously estimates the camera intrinsics, the ground plane, and physical distances between people from a single RGB image or video captured by a camera viewing a 3-D scene from a fixed vantage point. To automate camera calibration and distance estimation, we leverage priors about human pose and develop a novel direct formulation for pose-based auto-calibration and distance estimation, which shows state-of-the-art performance on publicly available datasets. The proposed approach enables existing camera systems to measure physical distances without needing a dedicated calibration process or range sensors, and is applicable to a broad range of use cases such as social distancing and workplace safety. Furthermore, to enable evaluation and drive research in this area, we contribute to the publicly available MEVA dataset with additional distance annotations, resulting in "MEVADA" – an evaluation benchmark for the pose-based auto-calibration and distance estimation problem.

Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and federated machine learning algorithms, which are poised to offer solutions. An international group of passionate researchers came together with a joint mission to solve the problem with our finest models and tools. The SCOR Consortium has developed a ready-to-deploy secure infrastructure using world-class privacy and security technologies to reconcile the privacy/utility conflicts. We hope our effort will make a change and accelerate research in future pandemics with broad and diverse samples on an international scale.