13,054 research outputs found
A Method to Improve the Early Stages of the Robotic Process Automation Lifecycle
The robotic automation of processes is of much interest to
organizations. A common use case is to automate the repetitive manual
tasks (or processes) that are currently done by back-office staff
through some information system (IS). The lifecycle of any Robotic Process
Automation (RPA) project starts with the analysis of the process
to automate. This is a very time-consuming phase, which in practical
settings often relies on the study of process documentation. Such documentation
is typically incomplete or inaccurate, e.g., some documented
cases never occur, occurring cases are not documented, or documented
cases differ from reality. To deploy robots in a production environment
that are designed on such a shaky basis entails a high risk. This paper
describes and evaluates a new proposal for the early stages of an RPA
project: the analysis of a process and its subsequent design. The idea is to
leverage the knowledge of back-office staff, which starts by monitoring
them in a non-invasive manner. This is done through a screen-mousekey-
logger, i.e., a sequence of images, mouse actions, and key actions
are stored along with their timestamps. The log which is obtained in
this way is transformed into a UI log through image-analysis techniques
(e.g., fingerprinting or OCR) and then transformed into a process model
by the use of process discovery algorithms. We evaluated this method for
two real-life, industrial cases. The evaluation shows clear and substantial
benefits in terms of accuracy and speed. This paper presents the method,
along with a number of limitations that need to be addressed such that
it can be applied in wider contexts.Ministerio de Economía y Competitividad TIN2016-76956-C3-2-
Geographical Counterpoint to Choreographic Information based on Approaches in GIScience and Visualization
This study provides geographical counterpoint to existing knowledge of a dance piece through approaches from GIScience and visualization by focusing on spatio-temporal movement of dancers in a large dataset of the dance. The goal of this study is to introduce a new application to bridging art and science in the domain of dance and geography disciplines. The study utilizes existing methodologies in GIScience, including exploratory spatial data analysis (ESDA), spatial analysis, Relative Motion (REMO) analysis, and Qualitative Trajectory Calculus (QTC) analysis for the reasoning of the dance data. The results of the study demonstrate the following. First, spatio-temporal information in the dance can be better understood by using approaches in geography, including ESDA, spatial analysis, REMO analysis, QTC analysis, and visualization. Second, the REMO analysis measured relative azimuth, speed, and δ-speed of the dancers per space and time and intuitively visualized their interactions. Third, the QTC analysis showed an example of measuring similarity and difference between repetitive movements of the dancers. The study exhibits how approaches of GIScience in geography could contribute to finding new knowledge of choreographic information that has been, in general, hard to recognize through other disciplines such as dance and statistics
CrossCode: Multi-level Visualization of Program Execution
Program visualizations help to form useful mental models of how programs
work, and to reason and debug code. But these visualizations exist at a fixed
level of abstraction, e.g., line-by-line. In contrast, programmers switch
between many levels of abstraction when inspecting program behavior. Based on
results from a formative study of hand-designed program visualizations, we
designed CrossCode, a web-based program visualization system for JavaScript
that leverages structural cues in syntax, control flow, and data flow to
aggregate and navigate program execution across multiple levels of abstraction.
In an exploratory qualitative study with experts, we found that CrossCode
enabled participants to maintain a strong sense of place in program execution,
was conducive to explaining program behavior, and helped track changes and
updates to the program state.Comment: 13 pages, 6 figures Submitted to CHI 2023: Conference on Human
Factors in Computing System
Reading out a spatiotemporal population code by imaging neighbouring parallel fibre axons in vivo.
The spatiotemporal pattern of synaptic inputs to the dendritic tree is crucial for synaptic integration and plasticity. However, it is not known if input patterns driven by sensory stimuli are structured or random. Here we investigate the spatial patterning of synaptic inputs by directly monitoring presynaptic activity in the intact mouse brain on the micron scale. Using in vivo calcium imaging of multiple neighbouring cerebellar parallel fibre axons, we find evidence for clustered patterns of axonal activity during sensory processing. The clustered parallel fibre input we observe is ideally suited for driving dendritic spikes, postsynaptic calcium signalling, and synaptic plasticity in downstream Purkinje cells, and is thus likely to be a major feature of cerebellar function during sensory processing
Implications of smartphone user privacy leakage from the advertiser’s perspective
Many smartphone apps routinely gather various private user data and send them to advertisers. Despite recent study on protection mechanisms and analysis on apps’ behavior, the understanding of the consequences of such privacy losses remains limited. In this paper, we investigate how much an advertiser can infer about users’ social and community relationships. After one month’s user study involving about 190 most popular Android apps, we find that an advertiser can infer 90% of the social relationships. We further propose a privacy leakage inference framework and use real mobility traces and Foursquare data to quantify the consequences of privacy leakage. We find that achieving 90% inference accuracy of the social and community relationships requires merely 3 weeks’ user data. Finally, we present a real-time privacy leakage visualization tool that captures and displays the spatial–temporal characteristics of the leakages. The discoveries underscore the importance of early adoption of privacy protection mechanisms
Bringing Context Inside Process Research with Digital Trace Data
Context is usually conceptualized as “external” to a theory or model and treated as something to be controlled or eliminated in empirical research. We depart from this tradition and conceptualize context as permeating processual phenomena. This move is possible because digital trace data are now increasingly available, providing rich and fine-grained data about processes mediated or enabled by digital technologies. This paper introduces a novel method for including fine-grained contextual information from digital trace data within the description of process (e.g., who, what, when, where, why). Adding contextual information can result in a very large number of fine-grained categories of events, which are usually considered undesirable. However, we argue that a large number of categories can make process data more informative for theorizing and that including contextual detail enriches the understanding of processes as they unfold. We demonstrate this by analyzing audit trail data of electronic medical records using ThreadNet, an open source software application developed for the qualitative visualization and analysis of process data. The distinctive contribution of our approach is the novel way in which we contextualize events and action in process data. Providing new, usable ways to incorporate context can help researchers ask new questions about the dynamics of processual phenomena
Characterization of the microvascular cerebral blood flow response to obstructive apneic events during night sleep
Peer ReviewedPostprint (author's final draft
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