Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 10133 publications
SAC124 - SSAC Advice on Name Collision Analysis
Internet Corporation for Assigned Names and Numbers (ICANN), ICANN Security and Stability Advisory Committee (SSAC) Reports and Advisories (2024), pp. 15
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In this document the Security and Stability Advisory Committee (SSAC) provides its analysis of
the findings and recommendations presented within the Name Collision Analysis Project
(NCAP) Study Two and the proposed Name Collision Risk Assessment Framework. The SSAC
also provides additional commentary on several aspects of the NCAP Study Two Report and
makes recommendations to the ICANN Board.
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PRewrite: Prompt Rewriting with Reinforcement Learning
Qiaozhu Mei
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (2024) (to appear)
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Prompt engineering is critical for the development of LLM-based applications. However, it is usually done manually in a "trial and error" fashion that can be time consuming, ineffective, and sub-optimal. Even for the prompts which seemingly work well, there is always a lingering question: can the prompts be made better with further modifications?
To address these problems, we investigate automated prompt engineering in this paper. Specifically, we propose PRewrite, an automated method to rewrite an under-optimized prompt to a more effective prompt. We instantiate the prompt rewriter using an LLM. The rewriter LLM is trained using reinforcement learning to optimize the performance on a given downstream task. We conduct experiments on diverse benchmark datasets, which demonstrates the effectiveness of PRewrite.
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In-Context Learning (ICL) is an emergent capability of Large Language Models (LLMs).
Only a few demonstrations enable LLMs to be used as blackbox for new tasks. Previous studies have shown that using LLMs' outputs as labels is effective in training models to select demonstrations. Such a label is expected to estimate utility of a demonstration in ICL;
however, it has not been well understood how different labeling strategies affect results on target tasks. This paper presents an analysis on different utility functions by focusing on LLMs' output probability given ground-truth output, and task-specific reward given LLMs' prediction. Unlike the previous work, we introduce a novel labeling method, incremental utility, which estimates how much incremental knowledge is brought into the LLMs by a demonstration. We conduct experiments with instruction-tuned LLMs on binary/multi-class classification, segmentation, and translation across Arabic, English, Finnish, Japanese, and Spanish. Our results show that (1) the probability is effective when the probability values are distributed across the whole value range (on the classification tasks), and (2) the downstream metric is more robust when nuanced reward values are provided with long outputs (on the segmentation and translation tasks). We then show that the proposed incremental utility further helps ICL by contrasting how the LLMs perform with and without the demonstrations.
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Verifying credentials, such as educational degrees, professional licenses, and permits, is a crucial yet challenging task for organizations globally. Traditional verification methods often rely on third-party vendors, introducing vulnerabilities like bias, security breaches, and privacy risks. While blockchain technology offers a promising solution for credential management, existing approaches often store sensitive credential data off-chain in centralized databases or InterPlanetary File System (IPFS), leaving them susceptible to data breaches and loss.
This paper presents a novel, privacy-preserving credential verification system built on a permissioned blockchain network. This system, implemented using the Hyperledger Fabric framework, offers several key advantages over traditional methods, including enhanced security and improved privacy. By leveraging cryptographic techniques, the system ensures the robust and privacypreserving storage of credentials directly on the blockchain. This eliminates the reliance on vulnerable off-chain storage and mitigates associated risks. Furthermore, our analysis of a common credential dataset demonstrates the practical feasibility and cost-effectiveness of our solution, suggesting its widespread adoption. By addressing the limitations of both traditional and existing blockchain-based approaches, our system provides a robust, secure, and efficient solution for credential management in diverse sectors.
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Large Scale Self-Supervised Pretraining for Active Speaker Detection
Alice Chuang
Keith Johnson
Wei Xia
Yunfan Ye
ICASSP 2024 (2024) (to appear)
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In this work we investigate the impact of a large-scale self-supervised pretraining strategy for active speaker detection (ASD) on an unlabeled dataset consisting of over 125k hours of YouTube videos. When compared to a baseline trained from scratch on much smaller in-domain labeled datasets we show that with pretraining we not only have a more stable supervised training due to better audio-visual features used for initialization, but also improve the ASD mean average precision by 23\% on a challenging dataset collected with Google Nest Hub Max devices capturing real user interactions.
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This is the seventh installment of the Developer Productivity for Humans column. This installment focuses on software quality: what it means, how developers see it, how we break it down into 4 types of quality, and the impact these have on each other.
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The evolution of AI is a pivotal moment in history, but it’s not the first time we have experienced technological advances that have changed how humans work. By looking at the advances in automobiles, we are reminded of the importance of focusing on our developers' needs and goals.
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Conversational AI in health: Design considerations from a Wizard-of-Oz dermatology case study with users, clinicians and a medical LLM
Brenna Li
Amy Wang
Patricia Strachan
Julie Anne Seguin
Sami Lachgar
Karyn Schroeder
Renee Wong
Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, pp. 10
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Although skin concerns are common, access to specialist care is limited. Artificial intelligence (AI)-assisted tools to support medical decisions may provide patients with feedback on their concerns while also helping ensure the most urgent cases are routed to dermatologists. Although AI-based conversational agents have been explored recently, how they are perceived by patients and clinicians is not well understood. We conducted a Wizard-of-Oz study involving 18 participants with real skin concerns. Participants were randomly assigned to interact with either a clinician agent (portrayed by a dermatologist) or an LLM agent (supervised by a dermatologist) via synchronous multimodal chat. In both conditions, participants found the conversation to be helpful in understanding their medical situation and alleviate their concerns. Through qualitative coding of the conversation transcripts, we provide insight on the importance of empathy and effective information-seeking. We conclude with design considerations for future AI-based conversational agents in healthcare settings.
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For Extended Reality (XR) headsets, a key aim is the natural interaction in 3D space beyond what traditional methods of keyboard, mouse, and touchscreen can offer. With the release of the Apple Vision Pro, a novel interaction paradigm is now widely available where users seamlessly navigate content through the combined use of their eyes and hands. However, blending these modalities poses unique design challenges due to their dynamic nature and the absence of established principles and standards.
In this article, we present five design principles and issues for the Gaze + Pinch interaction technique, informed by eye-hand research in the human-computer interaction field. The design principles encompass mechanisms like division of labor and minimalistic timing, which are crucial for usability, alongside enhancements for the manipulation of objects, indirect interactions, and drag & drop. Whether in design, technology, or research domains, this exploration offers valuable perspectives for navigating the evolving landscape of 3D interaction.
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SAC125 - SSAC Report on Registrar Nameserver Management
Gautam Akiwate
Tim April
kc claffy
Internet Corporation for Assigned Names and Numbers (ICANN), ICANN Security and Stability Advisory Committee (SSAC) Reports and Advisories (2024), pp. 56
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During domain registration, a minimum of two nameservers are typically required, and this
remains a requirement for any future updates to the domain. Often, domains are delegated to
nameservers that are subordinate to some other domains, creating inter-domain dependencies.
This network of dependencies creates a scenario where the functionality of a domain depends
on the operational status of another domain. This setup lacks contractual or procedural
safeguards against disruption or misuse, especially when the nameserver parent domain expires.
Most registries forbid deleting an expired domain if other domains depend on it for name
resolution. These constraints aim to prevent disruptions in DNS resolution for the dependent
domains. However, this also means that the expired domain remains in a liminal state, neither
fully operational nor completely removed. When registrars cannot delete expired domains with
dependents, they are forced to bear the burden of sponsoring the domain without remuneration
from the registrant. A peer-reviewed study, "Risky BIZness: Risks derived from Registrar Name
Management," observed that some registrars have found and utilized a loophole to these
constraints by renaming the host objects that are subordinate to the expiring domain.1 Once
renamed, the host objects are what Akiwate et al.—and subsequently the SSAC—refers to as
sacrificial nameservers.
This report focuses on a specific type of sacrificial nameserver where the parent domains of the renamed host objects are considered to be unsafe because they are registrable. Registrable
parent domains of sacrificial nameservers introduce a new attack surface for domain resolution
hijacking, as malicious actors can exploit unsafe sacrificial nameservers to gain unauthorized
control over the dependent domains, leading to manipulation or disruption. Unlike traditional
domain hijacking techniques that exploit compromised account credentials or manipulate the
resolution protocol, this report focuses on this unforeseen risk arising from a longstanding
practice employed by some registrars.
In this report, the SSAC explores potential solutions to remediate exposed domains and prevent
the creation of new unsafe sacrificial nameservers. The SSAC examines each proposed solution for its feasibility, effectiveness, and potential to reduce the attack surface without introducing undue complexity or new vulnerabilities into the DNS ecosystem.
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The Inside Story of Google’s Quiet Nuclear R&D Quest
IEEE Spectrum (2024)
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Examines how a Google R&D programme sought to accelerate a future of safer, cheaper and more ubiquitous fusion and other nuclear energy. Discusses how the programme was started, its major components: fusion, edge-of-technology, and policy advocacy supporting innovation. Shows successful exits for each part. Beyond telling the sotry, an intents is to show how to move the needle, and get people to think about how they might also help, and show Google has made a difference. Timing of publication marks the 10th anniversary of programme's start.
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API Governance at Scale
Mak Ahmad
JJ Geewax
David R Karger
Kwan-Liu Ma
ICSE 2024 Software Engineering in Practice (2024)
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API Governance, the process of applying standardized sets of policies and guardrails to the design and development of APIs, has only grown in importance and prominence given the continued growth in APIs being produced. In this paper, we present an Action Research style approach to investigate and understand the utility of a multi-faceted API Governance process being adopted inside Google. We first reflect on past research around API Governance, and then introduce three new components, 1. API Improvement Proposals (AIPs) the documented source of truth for API design rules, 2. API Linter, an automated analysis tool which checks for adherence to / violations of AIPs, and 3. API Readability, a program to educate and certify API design experts. These three components are designed to build upon pre-existing processes to scale and improve API design. Through a mixed-methods research strategy, containing both a survey and a series of interviews, we evaluate the utility of these approaches in supporting API Producers. Our research shows that API Producers have positive sentiment towards API Governance, validating the general direction of the program. Specifically, our study participants highlighted the positive impact of API Governance on the quality of the APIs they produced, via consistency in both the outcome and approach. This paper also discusses future research opportunities to enhance API Governance, specifically with regards to newer API Producers, who reported worse sentiment towards the program than their more experienced peers.
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Visual Grounding for User Interfaces
Yijun Qian
Yujie Lu
Alexander G. Hauptmann
2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2024) - Industry Track
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Enabling autonomous language agents to drive application user interfaces (UIs) as humans do can significantly expand the capability of today's API-based agents. Essential to this vision is the ability of agents to ground natural language commands to on-screen UI elements. Prior UI grounding approaches work by relaying on developer-provided UI metadata (UI trees, such as web DOM, and accessibility labels) to detect on-screen elements. However, such metadata is often unavailable or incomplete. Object detection techniques applied to UI screens remove this dependency, by inferring location and types of UI elements directly from the UI's visual appearance. The extracted semantics, however, are too limited to directly enable grounding. We overcome the limitations of both approaches by introducing the task of visual UI grounding, which unifies detection and grounding. A model takes as input a UI screenshot and a free-form language expression, and must identify the referenced UI element. We propose a solution to this problem, LVG, which learns UI element detection and grounding using a new technique called layout-guided contrastive learning, where the semantics of individual UI objects are learned also from their visual organization. Due to the scarcity of UI datasets, LVG integrates synthetic data in its training using multi-context learning. LVG outperforms baselines pre-trained on much larger datasets by over 4.9 points in top-1 accuracy, thus demonstrating its effectiveness.
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Photorealistic Video Generation with Diffusion Models
Agrim Gupta
Kihyuk Sohn
Xiuye Gu
Fei-Fei Li
Lu Jiang
ECCV (2024)
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We present W.A.L.T, a transformer-based approach for photorealistic video generation via diffusion modeling. Our approach has two key design decisions. First, we use a causal encoder to jointly compress images and videos within a unified latent space, enabling training and generation across modalities. Second, for memory and training efficiency, we use a window attention architecture tailored for joint spatial and spatiotemporal generative modeling. Taken together these design decisions enable us to achieve state-of-the-art performance on established video (UCF-101 and Kinetics-600) and image (ImageNet) generation benchmarks without using classifier free guidance. Finally, we also train a cascade of three models for the task of text-to-video generation consisting of a base latent video diffusion model, and two video super-resolution diffusion models to generate videos of 512*896 resolution at 8 frames per second.
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Assessing Web Fingerprinting Risk
Robert Busa-Fekete
Antonio Sartori
Proceedings of the ACM Web Conference (WWW 2024)
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Modern Web APIs allow developers to provide extensively customized experiences for website visitors, but the richness of the device information they provide also make them vulnerable to being abused by malign actors to construct browser fingerprints, device-specific identifiers that enable covert tracking of users even when cookies are disabled.
Previous research has established entropy, a measure of information, as the key metric for quantifying fingerprinting risk. Earlier studies that estimated the entropy of Web APIs were based on data from a single website or were limited to an extremely small sample of clients. They also analyzed each Web API separately and then summed their entropies to quantify overall fingerprinting risk, an approach that can lead to gross overestimates.
We provide the first study of browser fingerprinting which addresses the limitations of prior work. Our study is based on actual visited pages and Web API function calls reported by tens of millions of real Chrome browsers in-the-wild. We accounted for the dependencies and correlations among Web APIs, which is crucial for obtaining more realistic entropy estimates. We also developed a novel experimental design that accurately estimates entropy while never observing too much information from any single user. Our results provide an understanding of the distribution of entropy for different website categories, confirm the utility of entropy as a fingerprinting proxy, and offer a method for evaluating browser enhancements which are intended to mitigate fingerprinting.
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