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 10100 publications
SkipWriter: LLM-Powered Abbreviated Writing on Tablets
Zheer Xu
Mukund Varma T
Proceedings of UIST 2024 (2024)
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Large Language Models (LLMs) may offer transformative opportunities for text input, especially for physically demanding modalities like handwriting. We studied a form of abbreviated handwriting by designing, developing and evaluating a prototype, named SkipWriter, that convert handwritten strokes of a variable-length, prefix- based abbreviation (e.g., “ho a y” as handwritten strokes) into the intended full phrase (e.g., “how are you” in the digital format) based
on preceding context. SkipWriter consists of an in-production hand-writing recognizer and a LLM fine-tuned on this skip-writing task. With flexible pen input, SkipWriter allows the user to add and revise prefix strokes when predictions don’t match the user’s intent. An user evaluation demonstrated a 60% reduction in motor movements with an average speed of 25.78 WPM. We also showed that this reduction is close to the ceiling of our model in an offline simulation.
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The InterPlanetary File System (IPFS) is on its way to becoming the backbone of the next generation of the web. However, it suffers from several performance bottlenecks, particularly on the content retrieval path, which are often difficult to debug. This is because content retrieval involves multiple peers on the decentralized network and the issue could lie anywhere in the network. Traditional debugging tools are insufficient to help web developers who face the challenge of slow loading websites and detrimental user experience. This limits the adoption and future scalability of IPFS.
In this paper, we aim to gain valuable insights into how content retrieval requests propagate within the IPFS network as well as identify potential performance bottlenecks which could lead to opportunities for improvement. We propose a custom tracing framework that generates and manages traces for crucial events that take place on each peer during content retrieval. The framework leverages event semantics to build a timeline of each protocol involved in the retrieval, helping developers pinpoint problems. Additionally, it is resilient to malicious behaviors of the peers in the decentralized environment.
We have implemented this framework on top of an existing IPFS implementation written in Java called Nabu. Our evaluation shows that the framework can identify network delays and issues with each peer involved in content retrieval requests at a very low overhead.
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A Decade of Privacy-Relevant Android App Reviews: Large Scale Trends
Omer Akgul
Michelle Mazurek
Benoit Seguin
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We present an analysis of 12 million instances of privacy-relevant reviews publicly visible on the Google Play Store that span a 10 year period. By leveraging state of the art NLP techniques, we examine what users have been writing about privacy along multiple dimensions: time, countries, app types, diverse privacy topics, and even across a spectrum of emotions. We find consistent growth of privacy-relevant reviews, and explore topics that are trending (such as Data Deletion and Data Theft), as well as those on the decline (such as privacy-relevant reviews on sensitive permissions). We find that although privacy reviews come from more than 200 countries, 33 countries provide 90% of privacy reviews. We conduct a comparison across countries by examining the distribution of privacy topics a country’s users write about, and find that geographic proximity is not a reliable indicator that nearby countries have similar privacy perspectives. We uncover some countries with unique patterns and explore those herein. Surprisingly, we uncover that it is not uncommon for reviews that discuss privacy to be positive (32%); many users express pleasure about privacy features within apps or privacy-focused apps. We also uncover some unexpected behaviors, such as the use of reviews to deliver privacy disclaimers to developers. Finally, we demonstrate the value of analyzing app reviews with our approach as a complement to existing methods for understanding users' perspectives about privacy.
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Sharing is leaking: blocking transient-execution attacks with core-gapped confidential VMs
Charly Castes
29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4 (ASPLOS '24) (2024)
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Confidential VMs on platforms such as Intel TDX, AMD SEV and Arm CCA promise greater security for cloud users against even a hypervisor-level attacker, but this promise has been shattered by repeated transient-execution vulnerabilities and CPU bugs. At the root of this problem lies the need to multiplex CPU cores with all their complex microarchitectural state among distrusting entities, with an untrusted hypervisor in control of the multiplexing.
We propose core-gapped confidential VMs, a set of software-only modifications that ensure that no distrusting code shares a core, thus removing all same-core side-channels and transient-execution vulnerabilities from the guest’s TCB. We present an Arm-based prototype along with a performance evaluation showing that, not only does core-gapping offer performance competitive with non-confidential VMs, the greater locality achieved by avoiding shared cores can even improve performance for CPU-intensive workloads.
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Storage Systems For Real-Time Personalized Recommendations
Jayasekhar Konduru
Aqsa Fulara
DZone (2024)
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This article explores the demands of real-time personalized recommendation systems, focusing on data storage challenges and solutions. We'll present common storage solutions suitable for such systems and outline best practices.
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Traffic simulations: multi-city calibration of metropolitan highway networks
Yechen Li
Damien Pierce
27th IEEE International Conference on Intelligent Transportation Systems (ITSC) (2024)
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This paper proposes an approach to perform travel demand calibration for high-resolution stochastic traffic simulators. It employs abundant travel times at the path-level, departing from the standard practice of resorting to scarce segment-level sensor counts. The proposed approach is shown to tackle high-dimensional instances in a sample-efficient way. For the first time, case studies on 6 metropolitan highway networks are carried out, considering a total of 54 calibration scenarios. This is the first work to show the ability of a calibration algorithm to systematically scale across networks. Compared to the state-of-the-art simultaneous perturbation stochastic approximation (SPSA) algorithm, the proposed approach enhances fit to field data by an average 43.5% with a maximum improvement of 80.0%, and does so within fewer simulation calls.
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As AI systems quickly improve in both breadth and depth of performance, they lend themselves to creating increasingly powerful and realistic agents, including the possibility of agents modeled on specific people. We anticipate that within our lifetimes it may become common practice for people to create a custom AI agent to interact with loved ones and/or the broader world after death. We call these generative ghosts, since such agents will be capable of generating novel content rather than merely parroting content produced by their creator while living. In this paper, we first discuss the design space of potential implementations of generative ghosts. We then discuss the practical and ethical implications of generative ghosts, including potential positive and negative impacts on individuals and society. Based on these considerations, we lay out a research agenda for the AI and HCI research communities to empower people to create and interact with AI afterlives in a safe and beneficial manner.
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Bridging the Preference Gap between Retrievers and LLMs
Zixuan Ke
Qiaozhu Mei
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (2024) (to appear)
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Large Language Models (LLMs) have demonstrated superior results across a wide range of tasks, and Retrieval-augmented Generation (RAG) is an effective way to enhance the performance by locating relevant information and placing it into the context window of the LLM. However, the relationship between retrievers and LLM in a RAG is still under-investigated. Most existing work treats the retriever and the LLM as independent components and leaves a gap between retrieving human-"friendly" information and assembling a LLM-"friendly" context. In this work, we examine a novel bridge mechanism. We validate the ranking and selection assumptions of retrievers in the context of RAG and propose a framework that chains together supervised and reinforcement learning to train a bridge model that optimizes the connection between the retriever and the LLM. Empirical results demonstrate the effectiveness of our method in both question-answering and personalized generation tasks.
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Most of our interactions with digital content currently occur inside 2D screens, however moving from that format to immersive setups brings a paradigm shift. From content inside the screen to users inside the content. This change requires a revisit to how we blend the analog and the digital and how we transfer content between the two modes. Perhaps it even asks for new guidelines too. While different solutions appear in the space, the dynamic range only seems to widen. We can start to see what works and what does not work so well, in an empirical or ethnographic approach, beyond laboratory studies. But if we want to accelerate adoption we need to further the understanding on how current tasks can be improved. How this new form of interaction can increase their productivity. In this paper we focus on analyzing and converging what we think works, and envisioning how this new set of immersive devices and interactions can enable productivity beyond already existing tools.
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A scalable system to measure contrail formation on a per-flight basis
Erica Brand
Sebastian Eastham
Carl Elkin
Thomas Dean
Zebediah Engberg
Ulrike Hager
Joe Ng
Dinesh Sanekommu
Tharun Sankar
Marc Shapiro
Environmental Research Communications (2024)
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In this work we describe a scalable, automated system to determine from satellite data whether a given flight has made a persistent contrail.
The system works by comparing flight segments to contrails detected by a computer vision algorithm running on images from the GOES-16 Advanced Baseline Imager. We develop a `flight matching' algorithm and use it to label each flight segment as a `match' or `non-match'. We perform this analysis on 1.6 million flight segments and compare these labels to existing contrail prediction methods based on weather forecast data. The result is an analysis of which flights make persistent contrails several orders of magnitude larger than any previous work. We find that current contrail prediction models fail to correctly predict whether we will match a contrail in many cases.
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Unveiling Privacy Perspectives about Mobile Health Apps on a Large Scale
PETS workshop: Privacy, Safety and Trust for Mobile Health Apps (2024)
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In this paper we study users' opinions about the privacy of their mobile health apps. We look at what they write in app reviews in the 'Health & Fitness' category on the Google Play store. We identified 2832 apps in this category (based on 1K minimum installs). Using NLP/LLM analyses, we find that 76% of these apps have at least some privacy reviews. In total this yields over 164,000 reviews about privacy, from over 150 countries and in 25 languages. Our analyses identifies top themes and offers an approximation of how widespread these issues are around the world. We show that the top 4 themes - Data Sharing and Exposure, Permission Requests, Location Tracking and Data Collection - are issues of concern in over 70 countries. Our automatically generated thematic summaries reveal interesting aspects that deserve further research around user suspicions (unneeded data collection), user requests (more fine-grained control over data collection and data access), as well as user behavior (uninstalling apps).
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Predicting Cardiovascular Disease Risk using Photoplethysmography and Deep Learning
Sebastien Baur
Christina Chen
Mariam Jabara
Babak Behsaz
Shravya Shetty
Goodarz Danaei
Diego Ardila
PLOS Glob Public Health, 4(6) (2024), e0003204
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Cardiovascular diseases (CVDs) are responsible for a large proportion of premature deaths in low- and middle-income countries. Early CVD detection and intervention is critical in these populations, yet many existing CVD risk scores require a physical examination or lab measurements, which can be challenging in such health systems due to limited accessibility. We investigated the potential to use photoplethysmography (PPG), a sensing technology available on most smartphones that can potentially enable large-scale screening at low cost, for CVD risk prediction. We developed a deep learning PPG-based CVD risk score (DLS) to predict the probability of having major adverse cardiovascular events (MACE: non-fatal myocardial infarction, stroke, and cardiovascular death) within ten years, given only age, sex, smoking status and PPG as predictors. We compare the DLS with the office-based refit-WHO score, which adopts the shared predictors from WHO and Globorisk scores (age, sex, smoking status, height, weight and systolic blood pressure) but refitted on the UK Biobank (UKB) cohort. All models were trained on a development dataset (141,509 participants) and evaluated on a geographically separate test (54,856 participants) dataset, both from UKB. DLS’s C-statistic (71.1%, 95% CI 69.9–72.4) is non-inferior to office-based refit-WHO score (70.9%, 95% CI 69.7–72.2; non-inferiority margin of 2.5%, p<0.01) in the test dataset. The calibration of the DLS is satisfactory, with a 1.8% mean absolute calibration error. Adding DLS features to the office-based score increases the C-statistic by 1.0% (95% CI 0.6–1.4). DLS predicts ten-year MACE risk comparable with the office-based refit-WHO score. Interpretability analyses suggest that the DLS-extracted features are related to PPG waveform morphology and are independent of heart rate. Our study provides a proof-of-concept and suggests the potential of a PPG-based approach strategies for community-based primary prevention in resource-limited regions.
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Stereotypes are oversimplified beliefs and ideas about particular groups of people. These cognitive biases are omnipresent in our language, reflected in human-generated dataset and potentially learned and perpetuated by language technologies. Although mitigating stereotypes in language technologies is necessary for preventing harms, stereotypes can impose varying levels of risks for targeted individuals and social groups by appearing in various contexts. Technical challenges in detecting stereotypes are rooted in the societal nuances of stereotyping, making it impossible to capture all intertwined interactions of social groups in diverse cultural context in one generic benchmark. This paper delves into the nuances of detecting stereotypes in an annotation task with humans from various regions of the world. We iteratively disambiguate our definition of the task, refining it as detecting ``generalizing language'' and contribute a multilingual, annotated dataset consisting of sentences mentioning a wide range of social identities in 9 languages and labeled on whether they make broad statements and assumptions about those groups. We experiment with training generalizing language detection models, which provide insight about the linguistic context in which stereotypes can appear, facilitating future research in addressing the dynamic, social aspects of stereotypes.
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Predictive uncertainty-a model's self awareness regarding its accuracy on an input-is key for both building robust models via training interventions and for test-time applications such as selective classification. We propose a novel instance-conditioned reweighting approach that captures predictive uncertainty using an auxiliary network and unifies these train- and test-time applications. The auxiliary network is trained using a meta-objective in a bilevel optimization framework. A key contribution of our proposal is the meta-objective of minimizing the dropout variance, an approximation of Bayesian Predictive uncertainty. We show in controlled experiments that we effectively capture the diverse specific notions of uncertainty through this meta-objective, while previous approaches only capture certain aspects. These results translate to significant gains in real-world settings-selective classification, label noise, domain adaptation, calibration-and across datasets-Imagenet, Cifar100, diabetic retinopathy, Camelyon, WILDs, Imagenet-C,-A,-R, Clothing1M, etc. For Diabetic Retinopathy, we see upto 3.4%/3.3% accuracy and AUC gains over SOTA in selective classification. We also improve upon large-scale pretrained models such as PLEX.
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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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