scholarly journals Human Mobility Modelling Based on Dense Transit Areas Detection with Opportunistic Sensing

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Fernando Terroso-Sáenz ◽  
Mercedes Valdes-Vela ◽  
Aurora González-Vidal ◽  
Antonio F. Skarmeta
Keyword(s):  
Real Time ◽  
Real World ◽  
Large Scale ◽  
Human Mobility ◽  
Instrumental Approach ◽  
Mobile Crowdsensing ◽  
Urban Dynamics ◽  
Opportunistic Sensing ◽  
Novel Approach

With the advent of smartphones, opportunistic mobile crowdsensing has become an instrumental approach to perceive large-scale urban dynamics. In this context, the present work presents a novel approach based on such a sensing paradigm to automatically identify and monitor the areas of a city comprising most of the human transit. Unlike previous approaches, the system performs such detection in real time at the same time the opportunistic sensing is carried out. Furthermore, a novel multilayered grill partitioning to represent such areas is stated. Finally, the proposal is evaluated by means of a real-world dataset.

scholarly journals COVID-GAN+: Estimating Human Mobility Responses to COVID-19 through Spatio-temporal Generative Adversarial Networks with Enhanced Features

2022 ◽  
Vol 13 (2) ◽  
pp. 1-23
Author(s):  
Han Bao ◽  
Xun Zhou ◽  
Yiqun Xie ◽  
Yingxue Zhang ◽  
Yanhua Li
Keyword(s):  
Spatial Heterogeneity ◽  
Real World ◽  
Large Scale ◽  
Census Data ◽  
Human Mobility ◽  
Training Data ◽  
Estimation Accuracy ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Spatio Temporal

Estimating human mobility responses to the large-scale spreading of the COVID-19 pandemic is crucial, since its significance guides policymakers to give Non-pharmaceutical Interventions, such as closure or reopening of businesses. It is challenging to model due to complex social contexts and limited training data. Recently, we proposed a conditional generative adversarial network (COVID-GAN) to estimate human mobility response under a set of social and policy conditions integrated from multiple data sources. Although COVID-GAN achieves a good average estimation accuracy under real-world conditions, it produces higher errors in certain regions due to the presence of spatial heterogeneity and outliers. To address these issues, in this article, we extend our prior work by introducing a new spatio-temporal deep generative model, namely, COVID-GAN+. COVID-GAN+ deals with the spatial heterogeneity issue by introducing a new spatial feature layer that utilizes the local Moran statistic to model the spatial heterogeneity strength in the data. In addition, we redesign the training objective to learn the estimated mobility changes from historical average levels to mitigate the effects of spatial outliers. We perform comprehensive evaluations using urban mobility data derived from cell phone records and census data. Results show that COVID-GAN+ can better approximate real-world human mobility responses than prior methods, including COVID-GAN.

scholarly journals Self-regulation of stress-related large-scale brain network balance using real-time fMRI Neurofeedback

2021 ◽  
Author(s):  
Florian Krause ◽  
Nikolaos Kogias ◽  
Martin Krentz ◽  
Michael Luehrs ◽  
Rainer Goebel ◽  
...  
Keyword(s):  
Real Time ◽  
Executive Control ◽  
Large Scale ◽  
Acute Stress ◽  
Self Regulation ◽  
Real Life ◽  
Brain Network ◽  
Large Scale Network ◽  
Novel Approach ◽  
Scale Network

It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Maladaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance - a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience.

scholarly journals Real-world longitudinal data collected from the SleepHealth mobile app study

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Sean Deering ◽  
Abhishek Pratap ◽  
Christine Suver ◽  
A. Joseph Borelli ◽  
Adam Amdur ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Digital Health ◽  
The United States ◽  
Mobile App ◽  
Environmental Data ◽  
Sleep Habits ◽  
Real World Data ◽  
Disease Manifestation ◽  
Novel Approach

AbstractConducting biomedical research using smartphones is a novel approach to studying health and disease that is only beginning to be meaningfully explored. Gathering large-scale, real-world data to track disease manifestation and long-term trajectory in this manner is quite practical and largely untapped. Researchers can assess large study cohorts using surveys and sensor-based activities that can be interspersed with participants’ daily routines. In addition, this approach offers a medium for researchers to collect contextual and environmental data via device-based sensors, data aggregator frameworks, and connected wearable devices. The main aim of the SleepHealth Mobile App Study (SHMAS) was to gain a better understanding of the relationship between sleep habits and daytime functioning utilizing a novel digital health approach. Secondary goals included assessing the feasibility of a fully-remote approach to obtaining clinical characteristics of participants, evaluating data validity, and examining user retention patterns and data-sharing preferences. Here, we provide a description of data collected from 7,250 participants living in the United States who chose to share their data broadly with the study team and qualified researchers worldwide.

scholarly journals MG-DVD: A Real-time Framework for Malware Variant Detection Based on Dynamic Heterogeneous Graph Learning

2021 ◽  
Author(s):  
Chen Liu ◽  
Bo Li ◽  
Jun Zhao ◽  
Ming Su ◽  
Xu-Dong Liu
Keyword(s):  
Real Time ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Evolutionary Patterns ◽  
Graph Learning ◽  
Fine Grained ◽  
Variant Detection ◽  
Effectiveness And Efficiency ◽  
The Cost

Detecting the newly emerging malware variants in real time is crucial for mitigating cyber risks and proactively blocking intrusions. In this paper, we propose MG-DVD, a novel detection framework based on dynamic heterogeneous graph learning, to detect malware variants in real time. Particularly, MG-DVD first models the fine-grained execution event streams of malware variants into dynamic heterogeneous graphs and investigates real-world meta-graphs between malware objects, which can effectively characterize more discriminative malicious evolutionary patterns between malware and their variants. Then, MG-DVD presents two dynamic walk-based heterogeneous graph learning methods to learn more comprehensive representations of malware variants, which significantly reduces the cost of the entire graph retraining. As a result, MG-DVD is equipped with the ability to detect malware variants in real time, and it presents better interpretability by introducing meaningful meta-graphs. Comprehensive experiments on large-scale samples prove that our proposed MG-DVD outperforms state-of-the-art methods in detecting malware variants in terms of effectiveness and efficiency.

scholarly journals 590. Vancomycin Infusion: Algorithmic Analysis of Unstructured Real-World Data Captured from Automated Infusion Devices

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S358-S358
Author(s):  
David L Bostick ◽  
Kalvin Yu ◽  
Cynthia Yamaga ◽  
Ann Liu-Ferrara ◽  
Didier Morel ◽  
...  
Keyword(s):  
Real Time ◽  
Real World ◽  
Large Scale ◽  
Scale Up ◽  
Patient Specific ◽  
Care Practice ◽  
Becton Dickinson ◽  
Real World Data ◽  
Large Patient ◽  
Infusion Devices

Abstract Background Large scale research on antimicrobial usage in real-world populations traditionally does not consist of infusion data. With automation, detailed infusion events are captured in device systems, providing opportunities to harness them for patient safety studies. However, due to the unstructured nature of infusion data, the scale-up of data ingestion, cleansing, and processing is challenging. Figure 1. Illustration of dosing complexity Methods We applied algorithmic techniques to quantitate and visualize vancomycin administration data captured in real-time by automated infusion devices from 3 acute care hospitals. The device data included timestamped infusion events – infusion started, paused, restarted, alarmed, and stopped. We used time density-based segmentation algorithms to depict infusion sessions as bursts of event activity. We examined clinical interpretability of the cluster-defined sessions in defining infusion events, dosing intensity, and duration. Results The algorithms identified 13,339 vancomycin infusion sessions from 2,417 unique patients (mean = 5.5 sessions per patient). Clustering captured vancomycin infusion sessions consistently with correct event labels in >98% of cases. It disentangled ambiguity associated with unexpected events (e.g. multiple stopped/started events within a single infusion session). Segmentation of vancomycin infusion events on an example patient timeline is illustrated in Figure 1. The median duration of infusion sessions was 1.55 (1st, 3rd quartiles: 1.14, 2.02) hours, demonstrating clinical plausibility. Conclusion Passively captured vancomycin administration data from automated infusion device systems provide ramifications for real-time bed-side patient care practice. With large volume of data, temporal event segmentation can be an efficient approach to generate clinically interpretable insights. This method scales up accuracy and consistency in handling longitudinal dosing data. It can enable real-time population surveillance and patient-specific clinical decision support for large patient populations. Better understanding of infusion data may also have implications for vancomycin pharmacokinetic dosing. Disclosures David L. Bostick, PhD, Becton, Dickinson and Co. (Employee) Kalvin Yu, MD, Becton, Dickinson and Company (Employee)GlaxoSmithKline plc. (Other Financial or Material Support, Funding) Cynthia Yamaga, PharmD, BD (Employee) Ann Liu-Ferrara, PhD, Becton, Dickinson and Co. (Employee) Didier Morel, PhD, Becton, Dickinson and Co. (Employee) Ying P. Tabak, PhD, Becton, Dickinson and Co. (Employee)

scholarly journals Understanding the Heterogeneity of Human Mobility Patterns: User Characteristics and Modal Preferences

2021 ◽  
Vol 13 (24) ◽  
pp. 13921
Author(s):  
Laiyun Wu ◽  
Samiul Hasan ◽  
Younshik Chung ◽  
Jee Eun Kang
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Mobility Models ◽  
Mobility Patterns ◽  
User Characteristics ◽  
Transit System ◽  
Demographic Groups ◽  
Urban Dynamics ◽  
User Groups ◽  
Spatio Temporal

Characterizing individual mobility is critical to understand urban dynamics and to develop high-resolution mobility models. Previously, large-scale trajectory datasets have been used to characterize universal mobility patterns. However, due to the limitations of the underlying datasets, these studies could not investigate how mobility patterns differ over user characteristics among demographic groups. In this study, we analyzed a large-scale Automatic Fare Collection (AFC) dataset of the transit system of Seoul, South Korea and investigated how mobility patterns vary over user characteristics and modal preferences. We identified users’ commuting locations and estimated the statistical distributions required to characterize their spatio-temporal mobility patterns. Our findings show the heterogeneity of mobility patterns across demographic user groups. This result will significantly impact future mobility models based on trajectory datasets.

scholarly journals THE CHALLENGE OF MANAGING AND ANALYZING BIG DATA

2014 ◽  
pp. 204-209
Author(s):  
Hermann Heßling
Keyword(s):  
Image Processing ◽  
Big Data ◽  
Real Time ◽  
Research Group ◽  
Large Scale ◽  
Relevant Information ◽  
Time Dependent ◽  
Novel Approach ◽  
The Status ◽  
Real Time Computing

The amounts of data produced in science are growing exponentially. Traditional methods for storing and maintaining the enormous flood of data seem to be no longer sufficient anymore. The complexity of the data that will be distributed more and more worldwide, is going to constitute a considerable challenge for their analysis. According to Alex Szalay there soon will be produced so many data that they cannot even be stored and maintained anymore. The data have to be analyzed in real time in order to extract the relevant information. An outline of the project Large Scale Management and Analysis (LSDMA) is given. The status of our research group on distributed real-time computing is reviewed. Finally, a novel approach to time-dependent image processing based on local thermodynamical methods is presented.

scholarly journals Summarizing Source Code with Transferred API Knowledge

2018 ◽  
Author(s):  
Xing Hu ◽  
Ge Li ◽  
Xin Xia ◽  
David Lo ◽  
Shuai Lu ◽  
...  
Keyword(s):  
Real World ◽  
Software Maintenance ◽  
Large Scale ◽  
State Of The Art ◽  
Source Code ◽  
Code Search ◽  
Novel Approach ◽  
Software Maintenance And Evolution ◽  
World Industry ◽  
Similar Code

Code summarization, aiming to generate succinct natural language description of source code, is extremely useful for code search and code comprehension. It has played an important role in software maintenance and evolution. Previous approaches generate summaries by retrieving summaries from similar code snippets. However, these approaches heavily rely on whether similar code snippets can be retrieved, how similar the snippets are, and fail to capture the API knowledge in the source code, which carries vital information about the functionality of the source code. In this paper, we propose a novel approach, named TL-CodeSum, which successfully uses API knowledge learned in a different but related task to code summarization. Experiments on large-scale real-world industry Java projects indicate that our approach is effective and outperforms the state-of-the-art in code summarization.

Understanding Urban Dynamics from Taxi GPS Traces

2014 ◽  
pp. 299-317
Author(s):  
Lin Sun ◽  
Chao Chen ◽  
Daqing Zhang
Keyword(s):  
Travel Time ◽  
Large Scale ◽  
Human Mobility ◽  
Real Life ◽  
Urban Traffic ◽  
Mobility Patterns ◽  
Urban Dynamics ◽  
The Public ◽  
Taxi Service ◽  
Gps Traces

The GPS traces collected from a large taxi fleet provide researchers novel opportunities to inspect the urban dynamics in a city and lead to applications that can bring great benefits to the public. In this chapter, based on a real life large-scale taxi GPS dataset, the authors reveal the unique characteristics in the four different trace stages according to the passenger status, study the urban dynamics revealed in each stage, and explain the possible applications. Specifically, from passenger vacant traces, they study the taxi service dynamics, introduce how to use them to help taxis and passengers find each other, and reveal the work shifting dynamics in a city. From passenger occupied traces, they introduce their capabilities in monitoring and predicting urban traffic and estimating travel time. From the pick-up and drop-off events, the authors show the passenger hotspots and human mobility patterns in a city. They also consider taxis as mobile GPS sensors, which probe the urban road infrastructure dynamics.

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