Search

Clinical documentation can be transformed by Electronic Health Records, yet the documentation process is still a tedious, time-consuming, and error-prone process. Clinicians are faced with multi-faceted requirements and fragmented interfaces for information exploration and documentation. These challenges are only exacerbated in the Emergency Department -- clinicians often see 35 patients in one shift, during which they have to synthesize an often previously unknown patient's medical records in order to reach a tailored diagnosis and treatment plan. To better support this information synthesis, clinical documentation tools must enable rapid contextual access to the patient's medical record. MedKnowts is an integrated note-taking editor and information retrieval system which unifies the documentation and search process and provides concise synthesized concept-oriented slices of the patient's medical record. MedKnowts automatically captures structured data while still allowing users the flexibility of natural language. MedKnowts leverages this structure to enable easier parsing of long notes, auto-populated text, and proactive information retrieval, easing the documentation burden.

© 2020, VLDB Endowment. Automatic machine learning (AML) is a family of techniques to automate the process of training predictive models, aim-ing to both improve performance and make machine learn-ing more accessible. While many recent works have focused on aspects of the machine learning pipeline like model se-lection, hyperparameter tuning, and feature selection, rela-tively few works have focused on automatic data augmen-tation. Automatic data augmentation involves finding new features relevant to the user's predictive task with minimal "human-in-the-loop" involvement. We present ARDA, an end-to-end system that takes as input a dataset and a data repository, and outputs an aug-mented data set such that training a predictive model on this augmented dataset results in improved performance. Our system has two distinct components: (1) a framework to search and join data with the input data, based on various attributes of the input, and (2) an efficient feature selection algorithm that prunes out noisy or irrelevant features from the resulting join. We perform an extensive empirical eval-uation of different system components and benchmark our feature selection algorithm on real-world datasets.

Users have long struggled to extract and repurpose data from websites by laboriously copying or scraping content from web pages. An alternative is to write scripts that pull data through APIs. This provides a cleaner way to access data than scraping; however, APIs are effortful for programmers and nigh-impossible for non-programmers to use. In this work, we empower users to access APIs without programming. We evolve a schema for declaratively specifying how to interact with a data API. We then develop ScrAPIr: a standard query GUI that enables users to fetch data through any API for which a specification exists, and a second GUI that lets users author and share the specification for a given API. From a lab evaluation, we find that even non-programmers can access APIs using ScrAPIr, while programmers can access APIs 3.8 times faster on average using ScrAPIr than using programming.

New consent management platforms (CMPs) have been introduced to the web to conform with the EU's General Data Protection Regulation, particularly its requirements for consent when companies collect and process users' personal data. This work analyses how the most prevalent CMP designs affect people's consent choices. We scraped the designs of the five most popular CMPs on the top 10,000 websites in the UK (n=680). We found that dark patterns and implied consent are ubiquitous; only 11.8% meet our minimal requirements based on European law. Second, we conducted a field experiment with 40 participants to investigate how the eight most common designs affect consent choices. We found that notification style (banner or barrier) has no effect; removing the opt-out button from the first page increases consent by 22-23 percentage points; and providing more granular controls on the first page decreases consent by 8-20 percentage points. This study provides an empirical basis for the necessary regulatory action to enforce the GDPR, in particular the possibility of focusing on the centralised, third-party CMP services as an effective way to increase compliance., NSF (Award 1639994)

© 2018 Copyright held by the owner/author(s). The literate programming paradigm presents a program interleaved with natural language text explaining the code's rationale and logic. While this is great for program readers, the labor of creating literate programs deters most program authors from providing this text at authoring time. Instead, as we determine through interviews, developers provide their design rationales after the fact, in discussions with collaborators. We propose to capture these discussions and incorporate them into the code. We have prototyped a tool to link online discussion of code directly to the code it discusses. Incorporating these discussions incrementally creates post-literate programs that convey information to future developers.

© 2015 ACM. This paper introduces architectural and interaction patterns for integrating crowdsourced human contributions directly into user interfaces. We focus on writing and editing, complex endeavors that span many levels of conceptual and pragmatic activity. Authoring tools offer help with pragmatics, but for higher-level help, writers commonly turn to other people. We thus present Soylent, a word processing interface that enables writers to call on Mechanical Turk workers to shorten, proofread, and otherwise edit parts of their documents on demand. To improve worker quality, we introduce the Find-Fix-Verify crowd programming pattern, which splits tasks into a series of generation and review stages. Evaluation studies demonstrate the feasibility of crowdsourced editing and investigate questions of reliability, cost, wait time, and work time for edits.

Document clustering has not been well received as an information retrieval tool. Objections to its use fall into two main categories: first, that clustering is too slow for large corpora (with running time often quadratic in the number of documents); and second, that clustering does not appreciably improve retrieval. We argue that these problems arise only when clustering is used in an attempt to improve conventional search techniques. However, looking at clustering as an information access tool in its own right obviates these objections, and provides a powerful new access paradigm. We present a document browsing technique that employs docum-ent clustering as its primary operation. We also present fast (linear time) clustering algorithm.

© 2018 Copyright held by the owner/author(s). The literate programming paradigm presents a program interleaved with natural language text explaining the code's rationale and logic. While this is great for program readers, the labor of creating literate programs deters most program authors from providing this text at authoring time. Instead, as we determine through interviews, developers provide their design rationales after the fact, in discussions with collaborators. We propose to capture these discussions and incorporate them into the code. We have prototyped a tool to link online discussion of code directly to the code it discusses. Incorporating these discussions incrementally creates post-literate programs that convey information to future developers.

When users on social media share content without considering its veracity, they may unwittingly be spreading misinformation. In this work, we investigate the design of lightweight interventions that nudge users to assess the accuracy of information as they share it. Such assessment may deter users from posting misinformation in the first place, and their assessments may also provide useful guidance to friends aiming to assess those posts themselves. In support of lightweight assessment, we first develop a taxonomy of the reasons why people believe a news claim is or is not true; this taxonomy yields a checklist that can be used at posting time. We conduct evaluations to demonstrate that the checklist is an accurate and comprehensive encapsulation of people's free-response rationales. In a second experiment, we study the effects of three behavioral nudges---1) checkboxes indicating whether headings are accurate, 2) tagging reasons (from our taxonomy) that a post is accurate via a checklist and 3) providing free-text rationales for why a headline is or is not accurate---on people's intention of sharing the headline on social media. From an experiment with 1668 participants, we find that both providing accuracy assessment and rationale reduce the sharing of false content. They also reduce the sharing of true content, but to a lesser degree that yields an overall decrease in the fraction of shared content that is false. Our findings have implications for designing social media and news sharing platforms that draw from richer signals of content credibility contributed by users. In addition, our validated taxonomy can be used by platforms and researchers as a way to gather rationales in an easier fashion than free-response.

Determining affective states such as confusion from students' participation in online discussion forums can be useful for instructors of a large classroom. However, manual annotation of forum posts by instructors or paid crowd workers is both time-consuming and expensive. In this work, we harness affordances prevalent in social media to allow students to self-Annotate their discussion posts with a set of hashtags and emojis, a process that is fast and cheap. For students, selfannotation with hashtags and emojis provides another channel for self-expression, as well as a way to signal to instructors and other students on the lookout for certain types of messages. This method also provides an easy way to acquire a labeled dataset of affective states, allowing us distinguish between more nuanced emotions such as confusion and curiosity. From a dataset of over 25,000 discussion posts from two courses containing self-Annotated posts by students, we demonstrate how we can identify linguistic differences between posts expressing confusion versus curiosity, achieving 83% accuracy at distinguishing between the two affective states.

Users have long struggled to extract and repurpose data from websites by laboriously copying or scraping content from web pages. An alternative is to write scripts that pull data through APIs. This provides a cleaner way to access data than scraping; however, APIs are effortful for programmers and nigh-impossible for non-programmers to use. In this work, we empower users to access APIs without programming. We evolve a schema for declaratively specifying how to interact with a data API. We then develop ScrAPIr: a standard query GUI that enables users to fetch data through any API for which a specification exists, and a second GUI that lets users author and share the specification for a given API. From a lab evaluation, we find that even non-programmers can access APIs using ScrAPIr, while programmers can access APIs 3.8 times faster on average using ScrAPIr than using programming.

During the years, it has been possible to assess significant improvements in the computational efficiency of Semantic Web search and exploration systems. However, it has been much harder to assess how well different semantic systems’ user interfaces help their users. One of the key factors facilitating the advancement of research in a particular field is the ability to compare the performance of different approaches. Though there are many such benchmarks in Semantic Web fields that have experienced significant improvements, this is not the case for Semantic Web user interfaces for data exploration. We propose and demonstrate the use of a benchmark for evaluating such user interfaces, which includes a set of typical user tasks and a well-defined procedure for assigning a measure of performance on those tasks to a semantic system. We have applied the benchmark to four such systems. Moreover, all the required resources to apply the benchmark are openly available online. We intend to initiate a community conversation that will lead to a generally accepted framework for comparing systems and for measuring, and thus encouraging, progress towards better semantic search and exploration tools.

New consent management platforms (CMPs) have been introduced to the web to conform with the EU's General Data Protection Regulation, particularly its requirements for consent when companies collect and process users' personal data. This work analyses how the most prevalent CMP designs affect people's consent choices. We scraped the designs of the five most popular CMPs on the top 10,000 websites in the UK (n=680). We found that dark patterns and implied consent are ubiquitous; only 11.8% meet our minimal requirements based on European law. Second, we conducted a field experiment with 40 participants to investigate how the eight most common designs affect consent choices. We found that notification style (banner or barrier) has no effect; removing the opt-out button from the first page increases consent by 22-23 percentage points; and providing more granular controls on the first page decreases consent by 8-20 percentage points. This study provides an empirical basis for the necessary regulatory action to enforce the GDPR, in particular the possibility of focusing on the centralised, third-party CMP services as an effective way to increase compliance., NSF (Award 1639994)

Email management consumes significant effort from senders and recipients. Some of this work might be automatable. We performed a mixed-methods need-finding study to learn: (i) what sort of automatic email handling users want, and (ii) what kinds of information and computation are needed to support that automation. Our investigation included a design workshop to identify categories of needs, a survey to better understand those categories, and a classification of existing email automation software to determine which needs have been addressed. Our results highlight the need for: a richer data model for rules, more ways to manage attention, leveraging internal and external email context, complex processing such as response aggregation, and affordances for senders. To further investigate our findings, we developed a platform for authoring small scripts over a user’s inbox. Of the automations found in our studies, half are impossible in popular email clients, motivating new design directions.

Mavo is a small extension to the HTML language that empowers non-programmers to create simple web applications. Authors can mark up any normal HTML document with attributes that specify data elements that Mavo makes editable and persists. But while applications authored with Mavo allow users to edit individual data items, they do not offer any programmatic data actions that can act in customizable ways on large collections of data simultaneously or that modify data according to a computation. We explore an extension to the Mavo language that enables non-programmers to author these richer data update actions. We show that it lets authors create a more powerful set of applications than they could previously, while adding little additional complexity to the authoring process. Through user evaluations, we assess how closely our data update syntax matches how novice authors would instinctively express such actions, and how well they are able to use the syntax we provided.

Communication platforms have struggled to provide effective tools for people facing harassment online. We conducted interviews with 18 recipients of online harassment to understand their strategies for coping, finding that they often resorted to asking friends for help. Inspired by these findings, we explore the feasibility of friendsourced moderation as a technique for combating online harassment. We present Squadbox, a tool to help recipients of email harassment coordinate a "squad" of friend moderators to shield and support them during attacks. Friend moderators intercept email from strangers and can reject, organize, and redirect emails, as well as collaborate on filters. Squadbox is designed to let its users implement highly customized workflows, as we found in interviews that harassment and preferences for mitigating it vary widely. We evaluated Squadbox on five pairs of friends in a field study, finding that participants could comfortably navigate around privacy and personalization concerns.

Realtime crowdsourcing research has demonstrated that it is possible to recruit paid crowds within seconds by managing a small, fast-reacting worker pool. Realtime crowds enable crowd-powered systems that respond at interactive speeds: for example, cameras, robots and instant opinion polls. So far, these techniques have mainly been proof-of-concept prototypes: research has not yet attempted to understand how they might work at large scale or optimize their cost/performance trade-offs. In this paper, we use queueing theory to analyze the retainer model for realtime crowdsourcing, in particular its expected wait time and cost to requesters. We provide an algorithm that allows requesters to minimize their cost subject to performance requirements. We then propose and analyze three techniques to improve performance: push notifications, shared retainer pools, and precruitment, which involves recalling retainer workers before a task actually arrives. An experimental validation finds that precruited workers begin a task 500 milliseconds after it is posted, delivering results below the one-second cognitive threshold for an end-user to stay in flow.