scholarly journals Survey data and human computation for improved flu tracking

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Stefan Wojcik ◽  
Avleen S. Bijral ◽  
Richard Johnston ◽  
Juan M. Lavista Ferres ◽  
Gary King ◽  
...  
Keyword(s):  
Real Time ◽  
Survey Data ◽  
Human Behavior ◽  
Real World ◽  
Data Streams ◽  
Behavioral Model ◽  
Human Computation ◽  
Digital Data ◽  
Search Data ◽  
Tracking Model

AbstractWhile digital trace data from sources like search engines hold enormous potential for tracking and understanding human behavior, these streams of data lack information about the actual experiences of those individuals generating the data. Moreover, most current methods ignore or under-utilize human processing capabilities that allow humans to solve problems not yet solvable by computers (human computation). We demonstrate how behavioral research, linking digital and real-world behavior, along with human computation, can be utilized to improve the performance of studies using digital data streams. This study looks at the use of search data to track prevalence of Influenza-Like Illness (ILI). We build a behavioral model of flu search based on survey data linked to users’ online browsing data. We then utilize human computation for classifying search strings. Leveraging these resources, we construct a tracking model of ILI prevalence that outperforms strong historical benchmarks using only a limited stream of search data and lends itself to tracking ILI in smaller geographic units. While this paper only addresses searches related to ILI, the method we describe has potential for tracking a broad set of phenomena in near real-time.

A comparison of select trigger algorithms for automated global seismic phase and event detection

1998 ◽  
Vol 88 (1) ◽  
pp. 95-106 ◽  
Author(s):  
Mitchell Withers ◽  
Richard Aster ◽  
Christopher Young ◽  
Judy Beiriger ◽  
Mark Harris ◽  
...  
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Event Detection ◽  
Spectral Characteristics ◽  
Digital Data ◽  
Robust Detection ◽  
Location System ◽  
Global Correlation ◽  
Seismic Coda ◽  
Wide Range

Abstract Digital algorithms for robust detection of phase arrivals in the presence of stationary and nonstationary noise have a long history in seismology and have been exploited primarily to reduce the amount of data recorded by data logging systems to manageable levels. In the present era of inexpensive digital storage, however, such algorithms are increasingly being used to flag signal segments in continuously recorded digital data streams for subsequent processing by automatic and/or expert interpretation systems. In the course of our development of an automated, near-real-time, waveform correlation event-detection and location system (WCEDS), we have surveyed the abilities of such algorithms to enhance seismic phase arrivals in teleseismic data streams. Specifically, we have considered envelopes generated by energy transient (STA/LTA), Z-statistic, frequency transient, and polarization algorithms. The WCEDS system requires a set of input data streams that have a smooth, low-amplitude response to background noise and seismic coda and that contain peaks at times corresponding to phase arrivals. The algorithm used to generate these input streams from raw seismograms must perform well under a wide range of source, path, receiver, and noise scenarios. Present computational capabilities allow the application of considerably more robust algorithms than have been historically used in real time. However, highly complex calculations can still be computationally prohibitive for current workstations when the number of data streams become large. While no algorithm was clearly optimal under all source, receiver, path, and noise conditions tested, an STA/LTA algorithm incorporating adaptive window lengths controlled by nonstationary seismogram spectral characteristics was found to provide an output that best met the requirements of a global correlation-based event-detection and location system.

Digital Data Streams: Creating Value from the Real-Time Flow of Big Data

2016 ◽  
Vol 58 (3) ◽  
pp. 5-25 ◽  
Author(s):  
Federico Pigni ◽  
Gabriele Piccoli ◽  
Richard Watson
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Streams ◽  
Digital Data ◽  
The Real ◽  
Time Flow

scholarly journals Accurate estimation of influenza epidemics using Google search data via ARGO

2015 ◽  
Vol 112 (47) ◽  
pp. 14473-14478 ◽  
Author(s):  
Shihao Yang ◽  
Mauricio Santillana ◽  
S. C. Kou
Keyword(s):  
Real Time ◽  
Accurate Estimation ◽  
Online Search ◽  
Social Events ◽  
Search Data ◽  
Public Health Officials ◽  
Tracking Model ◽  
Real Time Tracking ◽  
Influenza Outbreaks ◽  
Google Search

Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistical foundation, ARGO outperforms all previously available Google-search–based tracking models, including the latest version of Google Flu Trends, even though it uses only low-quality search data as input from publicly available Google Trends and Google Correlate websites. ARGO not only incorporates the seasonality in influenza epidemics but also captures changes in people’s online search behavior over time. ARGO is also flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for real-time tracking of other social events at multiple temporal and spatial resolutions.

Improved Macro-clusters generation using Top-k shared Micro-clusters in Data Streams

2017 ◽  
Vol 7 (10) ◽  
pp. 52
Author(s):  
LAKSHMI PRANEETHA
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Bloom Filter ◽  
Scientific Applications ◽  
Pruning Algorithm ◽  
Density Data ◽  
Data Points ◽  
Short Time ◽  
Information Streams

Now-a-days data streams or information streams are gigantic and quick changing. The usage of information streams can fluctuate from basic logical, scientific applications to vital business and money related ones. The useful information is abstracted from the stream and represented in the form of micro-clusters in the online phase. In offline phase micro-clusters are merged to form the macro clusters. DBSTREAM technique captures the density between micro-clusters by means of a shared density graph in the online phase. The density data in this graph is then used in reclustering for improving the formation of clusters but DBSTREAM takes more time in handling the corrupted data points In this paper an early pruning algorithm is used before pre-processing of information and a bloom filter is used for recognizing the corrupted information. Our experiments on real time datasets shows that using this approach improves the efficiency of macro-clusters by 90% and increases the generation of more number of micro-clusters within in a short time.

A Simple and Practical Method for Incorporating Augmented Reality into the Classroom and Laboratory

2018 ◽  
Author(s):  
Kyle Plunkett
Keyword(s):  
Organic Chemistry ◽  
Augmented Reality ◽  
Real Time ◽  
Real World ◽  
Smart Phone ◽  
Practical Method ◽  
Virtual Expert ◽  
Video Projection

This manuscript provides two demonstrations of how Augmented Reality (AR), which is the projection of virtual information onto a real-world object, can be applied in the classroom and in the laboratory. Using only a smart phone and the free HP Reveal app, content rich AR notecards were prepared. The physical notecards are based on Organic Chemistry I reactions and show only a reagent and substrate. Upon interacting with the HP Reveal app, an AR video projection shows the product of the reaction as well as a real-time, hand-drawn curved-arrow mechanism of how the product is formed. Thirty AR notecards based on common Organic Chemistry I reactions and mechanisms are provided in the Supporting Information and are available for widespread use. In addition, the HP Reveal app was used to create AR video projections onto laboratory instrumentation so that a virtual expert can guide the user during the equipment setup and operation.

scholarly journals A Novel Rail-Network Hardware Simulator for Embedded System Programming

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Balaji M ◽  
Chandrasekaran M ◽  
Vaithiyanathan Dhandapani
Keyword(s):  
Embedded Systems ◽  
Embedded System ◽  
Real Time ◽  
Learning Outcomes ◽  
Real World ◽  
Time Constraints ◽  
Network Simulator ◽  
Practical Applications ◽  
Rail Network ◽  
Knowledge Enhancement

A Novel Rail-Network Hardware with simulation facilities is presented in this paper. The hardware is designed to facilitate the learning of application-oriented, logical, real-time programming in an embedded system environment. The platform enables the creation of multiple unique programming scenarios with variability in complexity without any hardware changes. Prior experimental hardware comes with static programming facilities that focus the students’ learning on hardware features and programming basics, leaving them ill-equipped to take up practical applications with more real-time constraints. This hardware complements and completes their learning to help them program real-world embedded systems. The hardware uses LEDs to simulate the movement of trains in a network. The network has train stations, intersections and parking slots where the train movements can be controlled by using a 16-bit Renesas RL78/G13 microcontroller. Additionally, simulating facilities are provided to enable the students to navigate the trains by manual controls using switches and indicators. This helps them get an easy understanding of train navigation functions before taking up programming. The students start with simple tasks and gradually progress to more complicated ones with real-time constraints, on their own. During training, students’ learning outcomes are evaluated by obtaining their feedback and conducting a test at the end to measure their knowledge acquisition during the training. Students’ Knowledge Enhancement Index is originated to measure the knowledge acquired by the students. It is observed that 87% of students have successfully enhanced their knowledge undergoing training with this rail-network simulator.

ViFin

2021 ◽  
Vol 5 (1) ◽  
pp. 1-25
Author(s):  
Wenqiang Chen ◽  
Lin Chen ◽  
Meiyi Ma ◽  
Farshid Salemi Parizi ◽  
Shwetak Patel ◽  
...  
Keyword(s):  
Real Time ◽  
Real World ◽  
Wearable Devices ◽  
User Studies ◽  
Finger Movement ◽  
Writing System ◽  
Time System ◽  
Real Time System ◽  
Input Devices ◽  
A New Technique

Wearable devices, such as smartwatches and head-mounted devices (HMD), demand new input devices for a natural, subtle, and easy-to-use way to input commands and text. In this paper, we propose and investigate ViFin, a new technique for input commands and text entry, which harness finger movement induced vibration to track continuous micro finger-level writing with a commodity smartwatch. Inspired by the recurrent neural aligner and transfer learning, ViFin recognizes continuous finger writing, works across different users, and achieves an accuracy of 90% and 91% for recognizing numbers and letters, respectively. We quantify our approach's accuracy through real-time system experiments in different arm positions, writing speeds, and smartwatch position displacements. Finally, a real-time writing system and two user studies on real-world tasks are implemented and assessed.

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

Assessing reinforcing versus aversive consequences in a real-time secondhand smoke intervention

2021 ◽  
Author(s):  
Vincent Berardi ◽  
John Bellettiere ◽  
Benjamin Nguyen ◽  
Neil E Klepeis ◽  
Suzanne C Hughes ◽  
...  
Keyword(s):  
Real Time ◽  
Real World ◽  
Secondhand Smoke ◽  
Relative Effectiveness ◽  
Feedback System ◽  
Control Group ◽  
Sensing Technology ◽  
Control Versus ◽  
Particle Counts ◽  
Post Hoc

Abstract Few studies have examined the relative effectiveness of reinforcing versus aversive consequences at changing behavior in real-world environments. Real-time sensing devices makes it easier to investigate such questions, offering the potential to improve both intervention outcomes and theory. This research aims to describe the development of a real-time, operant theory-based secondhand smoke (SHS) intervention and compare the efficacy of aversive versus aversive plus reinforcement contingency systems. Indoor air particle monitors were placed in the households of 253 smokers for approximately three months. Participants were assigned to a measurement-only control group (N = 129) or one of the following groups: 1.) aversive only (AO, N = 71), with aversive audio/visual consequences triggered by the detection of elevated air particle measurements, or 2.) aversive plus reinforcement (AP, N = 53), with reinforcing consequences contingent on the absence of SHS added to the AO intervention. Residualized change ANCOVA analysis compared particle concentrations over time and across groups. Post-hoc pairwise comparisons were also performed. After controlling for Baseline, Post-Baseline daily particle counts (F = 6.42, p = 0.002), % of time >15,000 counts (F = 7.72, p < 0.001), and daily particle events (F = 4.04, p = 0.02) significantly differed by study group. Nearly all control versus AO/AP pair-wise comparisons were statistically significant. No significant differences were found for AO versus AP groups. The aversive feedback system reduced SHS, but adding reinforcing consequences did not further improve outcomes. The complexity of real-world environments requires the nuances of these two contingency systems continue to be explored, with this study demonstrating that real-time sensing technology can serve as a platform for such research.

scholarly journals Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 349-371
Author(s):  
Hassan Mehmood ◽  
Panos Kostakos ◽  
Marta Cortes ◽  
Theodoros Anagnostopoulos ◽  
Susanna Pirttikangas ◽  
...  
Keyword(s):  
Real World ◽  
Data Streams ◽  
Smart City ◽  
Smart Cities ◽  
Concept Drift ◽  
Distributed Environment ◽  
Real World Data ◽  
Unique Challenge ◽  
World Data ◽  
Concept Drift Detection

Real-world data streams pose a unique challenge to the implementation of machine learning (ML) models and data analysis. A notable problem that has been introduced by the growth of Internet of Things (IoT) deployments across the smart city ecosystem is that the statistical properties of data streams can change over time, resulting in poor prediction performance and ineffective decisions. While concept drift detection methods aim to patch this problem, emerging communication and sensing technologies are generating a massive amount of data, requiring distributed environments to perform computation tasks across smart city administrative domains. In this article, we implement and test a number of state-of-the-art active concept drift detection algorithms for time series analysis within a distributed environment. We use real-world data streams and provide critical analysis of results retrieved. The challenges of implementing concept drift adaptation algorithms, along with their applications in smart cities, are also discussed.

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