scholarly journals Transfreq: a Python package for computing the theta-to-alpha transition frequency from resting state EEG data

2021 ◽  
Author(s):  
Elisabetta Vallarino ◽  
Sara Sommariva ◽  
Dario Arnaldi ◽  
Francesco Famà ◽  
Michele Piana ◽  
...  
Keyword(s):  
Resting State ◽  
Transition Frequency ◽  
Data Set ◽  
Open Source Data ◽  
Eeg Data ◽  
Source Data ◽  
Classic Approach ◽  
Spectral Profiles ◽  
Eeg Recordings ◽  
The Individual

AbstractA classic approach to estimate the individual theta-to-alpha transition frequency requires two electroencephalographic (EEG) recordings, one acquired in restingstate condition and one showing an alpha de-synchronisation due e.g. to task execution. This translates into longer recording sessions that my be cumbersome in studies involving patients. Moreover, incomplete de-synchronisation of the alpha rhythm may compromise the final estimation of the transition frequency. Here we present transfreq, a Python library that allows the computation of the transition frequency from resting-state data by clustering the spectral profiles at different EEG channels based on their content in the alpha and theta bands. We first provide an overview of the transfreq core algorithm and of the software architecture. Then we demonstrate its feasibility and robustness across different experimental setups on a publicly available EEG data set and on in-house recordings. A detailed documentation of transfreq and the codes for reproducing the analysis of the paper with the open-source data set are available online at https://elisabettavallarino.github.io/transfreq/

scholarly journals Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration

2019 ◽  
Vol 9 (17) ◽  
pp. 3558 ◽  
Author(s):  
Jinying Yu ◽  
Yuchen Gao ◽  
Yuxin Wu ◽  
Dian Jiao ◽  
Chang Su ◽  
...  
Keyword(s):  
Identification Accuracy ◽  
Data Set ◽  
Linear Discriminant ◽  
Practical Applications ◽  
Core Technology ◽  
Open Source Data ◽  
Source Data ◽  
Load Monitoring ◽  
Global Similarity ◽  
Linear Discriminant Classifier

Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to disaggregate the whole loads in a household, which leads to low identification accuracy. In this method, the event detection method is used to obtain the switching event sets of all loads, and the power consumption curves of independent unknown electrical appliances in a period are disaggregated by utilizing comprehensive features. A linear discriminant classifier group based on multi-feature global similarity is used for load identification. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. The simulation is carried out on an open source data set. The results demonstrate the effectiveness and high accuracy of the multi-feature integrated classification (MFIC) algorithm by using the state-of-the-art NILM methods as benchmarks.

Workshop Preview: Data Analytics and Machine Learning Hackathon 2021: A deep dive into the open-source data challenge for E&P

2021 ◽  
Vol 40 (1) ◽  
pp. 68-71
Author(s):  
Haibin Di ◽  
Anisha Kaul ◽  
Leigh Truelove ◽  
Weichang Li ◽  
Wenyi Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analytics ◽  
Learning Experience ◽  
Data Set ◽  
Deep Dive ◽  
Open Source Data ◽  
Research Workshop ◽  
Hands On ◽  
Source Data ◽  
Exploration And Production

We present a data challenge as part of the hackathon planned for the August 2021 SEG Research Workshop on Data Analytics and Machine Learning for Exploration and Production. The hackathon aims to provide hands-on machine learning experience for beginners and advanced practitioners, using a relatively well-defined problem and a carefully curated data set. The seismic data are from New Zealand's Taranaki Basin. The labels for a subset of the data have been generated by an experienced geologist. The objective of the challenge is to develop innovative machine learning solutions to identify key horizons.

Understanding the Role of Language in Conflict

2019 ◽  
Vol 64 (1) ◽  
pp. 97-117 ◽  
Author(s):  
William A. Donohue ◽  
Qi Hao ◽  
Richard Spreng ◽  
Charles Owen
Keyword(s):  
Text Analysis ◽  
Word Count ◽  
Data Set ◽  
Open Source Data ◽  
Communication Events ◽  
Source Data ◽  
Linguistic Inquiry ◽  
Mind Set ◽  
The Mind

The purpose of this article is to illustrate innovations in text analysis associated with understanding conflict-related communication events. Two innovations will be explored: LIWC (Linguistic Inquiry and Word Count), the text modeling program from the open-source data analysis software program R, and SPSS Modeler. The LIWC analysis revisits the 2009 study by Donohue and Druckman and the 2014 study by Donohue, Liang, and Druckman focusing on text analyses of the Oslo I Accords between the Palestinians and Israelis to illustrate this approach. The R and SPSS modeling of text analysis use the same data set as the LIWC analysis to provide a different set of pictures associated with each leader’s rhetoric during the period in which the Oslo I accords were being negotiated. Each innovation provides different insights into the mind-set of the two groups of leaders as the secret talks were emerging. The implications of each approach in establishing an understanding of the communication exchanges are discussed to conclude the article.

scholarly journals Assessing the sensitivity of EEG-based frequency-tagging as a metric for statistical learning

2021 ◽  
Author(s):  
Danna Pinto ◽  
Anat Prior ◽  
Elana Zion Golumbic
Keyword(s):  
Statistical Learning ◽  
Artificial Language ◽  
Group Level ◽  
Behavioral Measures ◽  
Individual Level ◽  
Non Invasive ◽  
Eeg Data ◽  
Eeg Recordings ◽  
The Individual ◽  
Low Sensitivity

Statistical Learning (SL) is hypothesized to play an important role in language development. However, the behavioral measures typically used to assess SL, particularly at the level of individual participants, are largely indirect and often have low sensitivity. Recently, a neural metric based on frequency-tagging has been proposed as an alternative and more direct measure for studying SL. Here we tested the sensitivity of frequency-tagging measures for studying SL in individual participants in an artificial language paradigm, using non-invasive EEG recordings of neural activity in humans. Importantly, we use carefully constructed controls, in order to address potential acoustic confounds of the frequency-tagging approach. We compared the sensitivity of EEG-based metrics to both explicit and implicit behavioral tests of SL, and the correspondence between these presumed converging operations. Group-level results confirm that frequency-tagging can provide a robust indication of SL for an artificial language, above and beyond potential acoustic confounds. However, this metric had very low sensitivity at the level of individual participants, with significant effects found only in 30% of participants. Conversely, the implicit behavior measures indicated that SL has occurred in 70% of participants, which is more consistent with the proposed ubiquitous nature of SL. Moreover, there was low correspondence between the different measures used to assess SL. Taken together, while some researchers may find the frequency-tagging approach suitable for their needs, our results highlight the methodological challenges of assessing SL at the individual level, and the potential confounds that should be taken into account when interpreting frequency-tagged EEG data.

scholarly journals Climate Change, Environmental Terrorism, Eco-Terrorism and Emerging Threats

2020 ◽  
Vol 13 (4) ◽  
pp. 58-80
Author(s):  
Paola Andrea Spadaro
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Civil Liberties ◽  
Security Threat ◽  
Terrorist Organizations ◽  
Social Events ◽  
Open Source Data ◽  
Source Data ◽  
The Individual ◽  
Violent Conflicts

The footprint of human advancement has accelerated a climate variability with no precedents, further driving devastating natural and social events. The decrease in basic resources like water, has already been identified as a driver of violent conflicts, which have given way to the strengthening of terrorist organizations that used the environment as a tool of coercion. The damage caused to the earth’s ecosystem has additionally raised a wave of defensive activism that was initially considered as eco-terrorism. While the original eco-movements had not induced fatalities, recent extremist organizations are showing a more violent anti-progress and pro-environment agenda. Added to these, the response of the states against environmental activism initiatives, has spiked some concerns over the repression of civil liberties, which may have the potential to fuel the angering of extreme individuals, who can be prompted to take radicalized action. Using open source data, this article shows that environmental terrorism represents an increasing security threat, which in the future, might be worsened by the individual radicalization of marginalized environmental extremists.

scholarly journals LTmatch: A Method to Abstract Pattern from Unstructured Log

2021 ◽  
Vol 11 (11) ◽  
pp. 5302
Author(s):  
Xiaodong Wang ◽  
Yining Zhao ◽  
Haili Xiao ◽  
Xiaoning Wang ◽  
Xuebin Chi
Keyword(s):  
Longest Common Subsequence ◽  
Practical Significance ◽  
Pattern Extraction ◽  
Data Set ◽  
Log Files ◽  
Open Source Data ◽  
Average Accuracy ◽  
Source Data ◽  
Matching Efficiency ◽  
Common Subsequence

Logs record valuable data from different software and systems. Execution logs are widely available and are helpful in monitoring, examination, and system understanding of complex applications. However, log files usually contain too many lines of data for a human to deal with, therefore it is important to develop methods to process logs by computers. Logs are usually unstructured, which is not conducive to automatic analysis. How to categorize logs and turn into structured data automatically is of great practical significance. In this paper, LTmatch algorithm is proposed, which implements a log pattern extracting algorithm based on a weighted word matching rate. Compared with our preview work, this algorithm not only classifies the logs according to the longest common subsequence(LCS) but also gets and updates the log template in real-time. Besides, the pattern warehouse of the algorithm uses a fixed deep tree to store the log patterns, which optimizes the matching efficiency of log pattern extraction. To verify the advantages of the algorithm, we applied the proposed algorithm to the open-source data set with different kinds of labeled log data. A variety of state-of-the-art log pattern extraction algorithms are used for comparison. The result shows our method is improved by 2.67% in average accuracy when compared with the best result in all the other methods.

scholarly journals An open-source data set of anti-VEGF therapy in diabetic macular oedema patients over 4 years and their visual acuity outcomes

Eye ◽  
2020 ◽  
Author(s):  
Christoph Kern ◽  
Dun Jack Fu ◽  
Josef Huemer ◽  
Livia Faes ◽  
Siegfried K. Wagner ◽  
...  
Keyword(s):  
Visual Acuity ◽  
Open Source ◽  
Macular Oedema ◽  
Diabetic Macular Oedema ◽  
Data Set ◽  
Anti Vegf ◽  
Open Source Data ◽  
Source Data

scholarly journals A Comparison of ORC-Compress Performance with Big Data Workload on Virtualization

2016 ◽  
Vol 855 ◽  
pp. 153-158
Author(s):  
Kritwara Rattanaopas ◽  
Sureerat Kaewkeerat ◽  
Yanapat Chuchuen
Keyword(s):  
Big Data ◽  
Execution Time ◽  
Large Data ◽  
Map Reduce ◽  
Data Set ◽  
Relational Information ◽  
Open Source Data ◽  
Space Saving ◽  
Source Data ◽  
Better Than

Big Data is widely used in many organizations nowadays. Hive is an open source data warehouse system for managing large data set. It provides a SQL-like interface to Hadoop over Map-Reduce framework. Currently, Big Data solution starts to adopt HiveQL tool to improve execution time of relational information. In this paper, we investigate on an execution time of query processing issues comparing two algorithm of ORC file: ZLIB and SNAPPY. The results show that ZLIB can compress data up to 87% compared to NONE compressing data. It was better than SNAPPY which has space saving 79%. However, the key for reducing execution time is Map-Reduce that were shown by a less query execution time when mapper and data node were equal. For example, all query suites in 6-node(ZLIB/SNAPPY) with 250-million table rows has quite similar execution time comparison to 9-node(ZLIB/SNAPPY) with 350-million table rows.

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