scholarly journals The Recorded Brahms Corpus (RBC): A Dataset of Performative Parameters in Recordings of Brahms's Cello Sonatas

2021 ◽  
Vol 16 (1) ◽  
pp. 124-133
Author(s):  
Ana Llorens
Keyword(s):  
Open Source ◽  
Performance Studies ◽  
Music Theory ◽  
Measurement Unit ◽  
Automatic Extraction ◽  
Manual Annotation ◽  
Dynamic Data ◽  
Dynamic Variations ◽  
Digital Formats ◽  
Tempo Fluctuations

This report describes the open-source Recorded Brahms Corpus (RBC) dataset, as well as the methods employed to extract and process the data. The dataset contains (micro)timing and dynamic data from 21 recordings of Brahms's Cello Sonatas, Opp. 38 and 99, focusing on note and beat onsets and duration, tempo fluctuations, and dynamic variations. Consistent manual annotation of the corpus in Sonic Visualiser was necessary prior to automatic extraction. Data for each recording and measurement unit are given as TXT files. Scores in various digital formats, the original SV files and diamond-shaped scape plots visualizations of the data are offered too. Expansion of the corpus with further movements of the sonatas, further recordings thereof and other compositions by Brahms is planned. The study of the data may contribute to performance studies and music theory alike.

scholarly journals rhizoTrak: a flexible open source Fiji plugin for user-friendly manual annotation of time-series images from minirhizotrons

2019 ◽  
Vol 444 (1-2) ◽  
pp. 519-534 ◽  
Author(s):  
Birgit Möller ◽  
Hongmei Chen ◽  
Tino Schmidt ◽  
Axel Zieschank ◽  
Roman Patzak ◽  
...  
Keyword(s):  
Time Series ◽  
Open Source ◽  
Manual Annotation ◽  
User Friendly ◽  
Time Series Images

scholarly journals Automated extraction of revision events from keystroke data

2021 ◽  
Author(s):  
Rianne Conijn ◽  
Emily Dux Speltz ◽  
Evgeny Chukharev-Hudilainen
Keyword(s):  
Writing Process ◽  
High Accuracy ◽  
Computational Approach ◽  
Automatic Extraction ◽  
Manual Annotation ◽  
Automated Extraction ◽  
Computational Approaches ◽  
Process Dynamics ◽  
Writing Task

AbstractRevision plays an important role in writing, and as revisions break down the linearity of the writing process, they are crucial in describing writing process dynamics. Keystroke logging and analysis have been used to identify revisions made during writing. Previous approaches include the manual annotation of revisions, building nonlinear S-notations, and the automated extraction of backspace keypresses. However, these approaches are time-intensive, vulnerable to construct, or restricted. Therefore, this article presents a computational approach to the automatic extraction of full revision events from keystroke logs, including both insertions and deletions, as well as the characters typed to replace the deleted text. Within this approach, revision candidates are first automatically extracted, which allows for a simplified manual annotation of revision events. Second, machine learning is used to automatically detect revision events. For this, 7120 revision events were manually annotated in a dataset of keystrokes obtained from 65 students conducting a writing task. The results showed that revision events could be automatically predicted with a relatively high accuracy. In addition, a case study proved that this approach could be easily applied to a new dataset. To conclude, computational approaches can be beneficial in providing automated insights into revisions in writing.

fsca

2021 ◽  
Vol 7 (2) ◽  
pp. 259-274 ◽  
Author(s):  
Nathan Vandeweerd
Keyword(s):  
Open Source ◽  
R Package ◽  
Manual Annotation ◽  
Syntactic Complexity ◽  
Complexity Measures ◽  
French Texts

Abstract This article reports on an open-source R package for the extraction of syntactic units from dependency-parsed French texts. To evaluate the reliability of the package, syntactic units were extracted from a corpus of L2 French and were compared to units extracted manually from the same corpus. The f-score of the extracted units ranged from 0.53–0.97. Although units were not always identical between the two methods, manual and automatically-derived syntactic complexity measures were strongly and significantly correlated (ρ = 0.62–0.97, p < 0.001), suggesting that this package may be a suitable replacement for manual annotation in some cases where manual annotation is not possible but that care should be used in interpreting the measures based on these units.

scholarly journals Sensor-Based Extraction Approaches of In-Vehicle Information for Driver Behavior Analysis

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5197
Author(s):  
Beomjun Kim ◽  
Yunju Baek
Keyword(s):  
Behavior Analysis ◽  
Electronic Control Unit ◽  
Driver Behavior ◽  
Sensor Data ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Steering Wheel ◽  
Automatic Extraction ◽  
Area Network ◽  
Vehicle Information

Advances in vehicle technology have resulted in the development of vehicles equipped with sensors to acquire standardized information such as engine speed and vehicle speed from the in-vehicle controller area network (CAN) system. However, there are challenges in acquiring proprietary information from CAN frames, such as the brake pedal and steering wheel operation, which are essential for driver behavior analysis. Such information extraction requires electronic control unit identifier analysis and accompanying data interpretation. In this paper, we present a system for the automatic extraction of proprietary in-vehicle information using sensor data correlated with the desired information. First, the proposed system estimates the vehicle’s driving status through threshold-, random forest-, and long short-term memory-based techniques using inertial measurement unit and global positioning system values. Then, the system segments in-vehicle CAN frames using the estimation and evaluates each segment with our scoring method to select suitable candidates by examining the similarity between each candidate and its estimation through the suggested distance matching technique. We conduct comprehensive experiments of the proposed system using real vehicles in an urban environment. Performance evaluation shows that the estimation accuracy of the driving condition is 84.20%, and the extraction accuracy of the in-vehicle information is 82.31%, which implies that the presented approaches are quite feasible for automatic extraction of proprietary in-vehicle information.

scholarly journals Methodology for Data-Informed Process Improvement to Enable Automated Manufacturing in Current Manual Processes

2021 ◽  
Vol 11 (9) ◽  
pp. 3889
Author(s):  
Mumtahina Mahajabin Adrita ◽  
Alexander Brem ◽  
Dominic O’Sullivan ◽  
Eoin Allen ◽  
Ken Bruton
Keyword(s):  
Human Error ◽  
Lean Six Sigma ◽  
Manufacturing Industries ◽  
Data Conversion ◽  
Automatic Extraction ◽  
The Novel ◽  
Digital Formats ◽  
Using Data ◽  
Product Output ◽  
Data Informed

Manufacturing industries are constantly identifying ways to automate machinery and processes to reduce waste and increase profits. Machines that were previously handled manually in non-standardized manners can now be automated. Converting non-digital records to digital formats is called digitization. Data that are analyzed or entered manually are subject to human error. Digitization can remove human error, when dealing with data, via automatic extraction and data conversion. This paper presents methodology to identify automation opportunities and eliminate manual processes via digitized data analyses. The method uses a hybrid combination of Lean Six Sigma (LSS), CRISP-DM framework, and “pre-automation” sequence, which address the gaps in each individual methodology and enable the identification and analysis of processes for optimization, in terms of automation. The results from the use case validates the novel methodology, reducing the implant manufacturing process cycle time by 3.76%, with a 4.48% increase in product output per day, as a result of identification and removal of manual steps based on capability studies. This work can guide manufacturing industries in automating manual production processes using data digitization.

scholarly journals NINscope: a versatile miniscope for multi-region circuit investigations

2019 ◽  
Author(s):  
Andres de Groot ◽  
Bastijn J.G. van den Boom ◽  
Romano M. van Genderen ◽  
Joris Coppens ◽  
John van Veldhuijzen ◽  
...  
Keyword(s):  
Open Source ◽  
Dorsal Striatum ◽  
High Sensitivity ◽  
Brain Regions ◽  
Image Sensor ◽  
Measurement Unit ◽  
Led Driver ◽  
Optogenetic Stimulation ◽  
Subcortical Region ◽  
Cortical Regions

AbstractMiniaturized fluorescence microscopes (miniscopes) have been instrumental to monitor neural activity during unrestrained behavior and their open-source versions have helped to distribute them at an affordable cost. Generally, the footprint and weight of open-source miniscopes is sacrificed for added functionality. Here, we present NINscope: a light-weight, small footprint, open-source miniscope that incorporates a high-sensitivity image sensor, an inertial measurement unit (IMU), and an LED driver for an external optogenetic probe. We highlight the advantages of NINscope by performing the first simultaneous cellular resolution (dual scope) recordings from cerebellum and cerebral cortex in unrestrained mice, revealing that the activity of both regions generally precedes the onset of behavioral acceleration. We further demonstrate the optogenetic stimulation capabilities of NINscope and show that cerebral cortical activity can be driven strongly by cerebellar stimulation. To validate the performance of our miniscope to image from deep-brain regions, we recorded in the dorsal striatum and, using the IMU to assess turning movements, replicate previous studies that show encoding of action space in this subcortical region. Finally, we combine optogenetic stimulation of distinct cortical regions projecting to the dorsal striatum, to probe functional connectivity. In combination with cross-platform control software, NINscope is a versatile addition to the expanding toolbox of open-source miniscopes and will aid multi-region circuit investigations during unrestrained behavior.

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