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Before submitting your paper please check the following:
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Impact Factor sufficiently large, Response Time sufficiently small
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Have you read the instructions for authors? How many pages, one or two columns, ...
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Is your paper, a survey, a magazine paper, a research paper, a research letter? Expectations of the journal? Coverage of the journal in terms of topics and style, ...
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Have you seen several similar papers in the same journal so that you can follow the desired style: similar section titles, number of references, target audience, ...
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Attractive Title
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Strong, Attracting (to the point, containing originality) Abstract. Please remember that Abstract is like a mini/micro paper. It should contain the motivation, originality, methods and hint of results.
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Introduction: is it focused, does it have the motivation, does it end with the contributions and layout of the paper?
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Does the introduction provide sufficient background and include
relevant references
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A use case may help describing the motivation
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Arguments why this approach is particularly suitable, or how it relates to others
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Novelty, list of contributions,
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In the Related Works Section: Novelty demonstrating related works table, this section should not be a concatanation of short sentences from each referance. The last row of the table will contain your proposal and clearly demonstrate the differences and originiality of your work.
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Is it scalable? easy to apply to the population?
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Problem Description if needed (otherwise motivation describes the need for this work
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Except Intro and Concl. non-generic section titles should be preffered
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Some journals expect Background and/or Methodology as separate sections
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System/Solution description (including a high level diagram, figure), methodology
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Sufficient detail about each subsystem or stage
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Information on preprocessing, feature extraction, classification Framework
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Information about the smartwatch, device etc. How data is handled?
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Questionnaire explanation and justificaiton? Which threshold for
classification? Can we apply regression?
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Why these signals? The justification should be provided?
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Raw data, example of data cleaning
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Comparision of raw signals in different classes?
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Justification of window size/type/overlap
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Why these features? (References)
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Feature Selection?
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How machine learning models formed?
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Classification? Parameter selection? Are the parameters fine tuned?
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Cross validation? Separate Test set?
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Are the methods adequately described?
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Results starting with Experiment Design
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Total Size of the data? How many participants? How many hours, days.
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Demography? Age, Gender of the participants
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Ethics/consent procedure. Shoulde be in a Consent Folder.
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Handling of imbalance? Deletion of majority? Smote?
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How is the data treated when there are missing labels?
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How is data treated when it contains noise?
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How many trials were collected per participant per day and
How many trials were collected per participant
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The distribution of data in different states/classes
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Details of data collection
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How many participant completed? How many taken out of study?
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Is the research design appropriate?
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Are the results clearly presented? Just the facts. No speculation. Figures and tables but not both for the same results.
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Are the conclusions supported by the results?
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The system/solution should be compared with other system that was used
in real-life situation in the context of key parameters such as
accuracies, algorithms, signal qualities, and complicity in data processing.
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Not only acccuracy, AUC, F-measure, Recall, ERR, etc.
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Comments on results (discussion)
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Limitations?
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To the point Conclusions stressing novelty, significant findings? Do we compare with prior works?
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Reviewers should not say: Countless Spelling mistakes :)) Grammarly like services can be used
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References: sufficiently many, fresh, preferably containing several from the target journal
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Acknowledgments with propoer project numbers.
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Biographies and pictures if needed
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Data and the code for reviewers? Maybe at least feature vector....
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Paper Submission Checklist v1.2 6 January 2020
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