6,779 research outputs found

    Proactive Information Retrieval via Screen Surveillance

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    We demonstrate proactive information retrieval via screen surveillance. A user's digital activities are continuously monitored by capturing all content on a user's screen using optical character recognition. This includes all applications and services being exploited and relies on each individual user's computer usage, such as their Web browsing, emails, instant messaging, and word processing. Topic modeling is then applied to detect the user's topical activity context to retrieve information. We demonstrate a system that proactively retrieves information from a user's activity history being observed on the screen when the user is performing unseen activities on a personal computer. We report an evaluation with ten participants that shows high user satisfaction and retrieval effectiveness. Our demonstration and experimental results show that surveillance of a user's screen can be used to build an extremely rich model of a user's digital activities across application boundaries and enable effective proactive information retrieval.Peer reviewe

    Watching inside the Screen: Digital Activity Monitoring for Task Recognition and Proactive Information Retrieval

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    We investigate to what extent it is possible to infer a user’s work tasks by digital activity monitoring and use the task models for proactive information retrieval. Ten participants volunteered for the study, in which their computer screen was monitored and related logs were recorded for 14 days. Corresponding diary entries were collected to provide ground truth to the task detection method. We report two experiments using this data. The unsupervised task detection experiment was conducted to detect tasks using unsupervised topic modeling. The results show an average task detection accuracy of more than 70% by using rich screen monitoring data. The single-trial task detection and retrieval experiment utilized unseen user inputs in order to detect related work tasks and retrieve task-relevant information on-line. We report an average task detection accuracy of 95%, and the corresponding model-based document retrieval with Normalized Discounted Cumulative Gain of 98%. We discuss and provide insights regarding the types of digital tasks occurring in the data, the accuracy of task detection on different task types, and the role of using different data input such as application names, extracted keywords, and bag-of-words representations in the task detection process. We also discuss the implications of our results for ubiquitous user modeling and privacy.Peer reviewe

    Autonomic care platform for optimizing query performance

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    Background: As the amount of information in electronic health care systems increases, data operations get more complicated and time-consuming. Intensive Care platforms require a timely processing of data retrievals to guarantee the continuous display of recent data of patients. Physicians and nurses rely on this data for their decision making. Manual optimization of query executions has become difficult to handle due to the increased amount of queries across multiple sources. Hence, a more automated management is necessary to increase the performance of database queries. The autonomic computing paradigm promises an approach in which the system adapts itself and acts as self-managing entity, thereby limiting human interventions and taking actions. Despite the usage of autonomic control loops in network and software systems, this approach has not been applied so far for health information systems. Methods: We extend the COSARA architecture, an infection surveillance and antibiotic management service platform for the Intensive Care Unit (ICU), with self-managed components to increase the performance of data retrievals. We used real-life ICU COSARA queries to analyse slow performance and measure the impact of optimizations. Each day more than 2 million COSARA queries are executed. Three control loops, which monitor the executions and take action, have been proposed: reactive, deliberative and reflective control loops. We focus on improvements of the execution time of microbiology queries directly related to the visual displays of patients' data on the bedside screens. Results: The results show that autonomic control loops are beneficial for the optimizations in the data executions in the ICU. The application of reactive control loop results in a reduction of 8.61% of the average execution time of microbiology results. The combined application of the reactive and deliberative control loop results in an average query time reduction of 10.92% and the combination of reactive, deliberative and reflective control loops provides a reduction of 13.04%. Conclusions: We found that by controlled reduction of queries' executions the performance for the end-user can be improved. The implementation of autonomic control loops in an existing health platform, COSARA, has a positive effect on the timely data visualization for the physician and nurse

    Prospective Memory in the Red Zone: Cognitive Control and Capacity Sharing in a Complex, Multi-Stimulus Task

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    © 2019 American Psychological Association. Remembering to perform a planned action upon encountering a future event requires event-based Prospective Memory (PM). PM is required in many human factors settings in which operators must process a great deal of complex, uncertain information from an interface. We study event-based PM in such an environment. Our task, which previous research has found is very demanding (Palada, Neal, Tay, & Heathcote, 2018), requires monitoring ships as they cross the ocean on a display. We applied the Prospective Memory Decision Control Model (Strickland, Loft, Remington, & Heathcote, 2018) to understand the cognitive mechanisms that underlie PM performance in such a demanding environment. We found evidence of capacity sharing between monitoring for PM items and performing the ongoing surveillance task, whereas studies of PM in simpler paradigms have not (e.g., Strickland et al., 2018). We also found that participants applied proactive and reactive control (Braver, 2012) to adapt to the demanding task environment. Our findings illustrate the value of human factors simulations to study capacity sharing between competing task processes. They also illustrate the value of cognitive models to illuminate the processes underlying adaptive behavior in complex environments

    Entity Recommendation for Everyday Digital Tasks

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    Recommender systems can support everyday digital tasks by retrieving and recommending useful information contextually. This is becoming increasingly relevant in services and operating systems. Previous research often focuses on specific recommendation tasks with data captured from interactions with an individual application. The quality of recommendations is also often evaluated addressing only computational measures of accuracy, without investigating the usefulness of recommendations in realistic tasks. The aim of this work is to synthesize the research in this area through a novel approach by (1) demonstrating comprehensive digital activity monitoring, (2) introducing entity-based computing and interaction, and (3) investigating the previously overlooked usefulness of entity recommendations and their actual impact on user behavior in real tasks. The methodology exploits context from screen frames recorded every 2 seconds to recommend information entities related to the current task. We embodied this methodology in an interactive system and investigated the relevance and influence of the recommended entities in a study with participants resuming their real-world tasks after a 14-day monitoring phase. Results show that the recommendations allowed participants to find more relevant entities than in a control without the system. In addition, the recommended entities were also used in the actual tasks. In the discussion, we reflect on a research agenda for entity recommendation in context, revisiting comprehensive monitoring to include the physical world, considering entities as actionable recommendations, capturing drifting intent and routines, and considering explainability and transparency of recommendations, ethics, and ownership of data
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