Journal Track abstract submission guidelines:

For MLJ the authors submit through the following EasyChair link: https://easychair.org/conferences/?conf=dsaa2024.
Then create a new submission and select "MLJ Journal Track Abstract", then update the author information and pdf abstract directly. You will receive a separated email generated by EasyChair for IEEE Copyright form.

Alternatively, if you the corresponding authors who received invited submission link before, please proceed with submission by select "DSAA 2024" as the proceeding, update the author list and complete your submission. Please pick one of the two methods avoid duplication submissions.

Please refer to the camera ready papge for the page limit, pdf preparation, and complete the conference registration.

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We invite submissions to the journal track of the 2024 IEEE International Conference on Data Science and Advanced Analytics (DSAA'2024) with Machine Learning Journal. The DSAA conference provides an international forum for the discussion on the latest high-quality research results in all theoretical and practical areas related to data science and advanced analytics, which span across various disciplines including statistics, learning, analytics, computing, and informatics, and domains from business to government, health and medicine, and social problems. The DSAA'2024 Journal Track with Machine Learning Journal (MLJ) seeks original, unpublished high-quality submissions, where the accepted papers will be published in MLJ with an extended abstract included in the DSAA'2024 proceedings.

Topics of Interest

We welcome original and well-grounded research papers on all aspects of foundations of data science, analytics, and machine learning, including but not limited to the following topics:

• Analytical and learning foundation
• Mathematical foundations for data science and analytics
• Statistical foundations for data science and analytics
• Physics-informed modeling, analytics and learning
• Automated analytics, learning, and inference
• Feature engineering, embedding and mining
• Representation learning
• Non-IID learning, nonstationary, coupled and entangled learning
• Heterogeneous, mixed, multimodal, multi-view and multi-distributional learning
• Online, streaming, dynamic and real-time learning
• Causality and causal learning
• Reinforcement learning including with human feedback
• Multi-instance, multi-label, multi-class and multi-target learning
• Unsupervised, semi-supervised and weakly supervised learning
• Learning complex interactions, couplings, and relations
• Deep learning theories and models
• Large multimodal modeling
• Learning from network and graph data
• Learning from data with domain and web knowledge
• Autonomous learning and optimization systems
• Open world (object, domain, set, task etc) learning
• Impactful analytical and learning applications
• Learning to fuse data/information from disparate sources
• Understanding data characteristics and complexities
• Complex data preprocessing, manipulation and augmentation
• Social, economic, financial and cultural analytics
• Graph and network embedding, analysis, learning and mining
• Machine learning for recommendation, marketing and online business
• Data-driven computer vision and image processing
• Cybersecurity and information disorder, misinformation/fake detection
• Analytics and learning for IoT, smart city, smart home, telecommunications, 5G and mobile services
• Government and enterprise data science
• Analytics and learning for transportation, manufacturing, procurement, and Industry 4.0
• Analytics and learning for energy, smart grids and renewable energies
• Agricultural, environmental, climate and spatio-temporal analytics
• Human-centered and domain-driven analytics and learning
• Fairness, explainability and algorithm bias
• Risk, compliance, regulation, anomaly, debt, failure and crisis analysis
• Privacy, ethics, transparency, accountability, responsibility, trust, reproducibility and retractability
• Green and energy-efficient, scalable, cloud/distributed and parallel analytics

Other topics closely relevant to the scope of DSAA and MLJ would also be considered.

Important Dates

• Paper submission due: April 15, 2024, and May 19, 2024
• Final paper notification due: July 24, 2024
• MLJ revision submission due: August 21, 2024
• DSAA'2024 extended abstract due: August 21, 2024

Double-blind Review Paper Submission

To submit to this track, authors have to make a journal submission to the CMT submission system by providing the title, abstract, the author information, the research areas related to the paper.

Templates for preparing your submissions to the DSAA'2024 Journal Track with MLJ can be found at https://www.springernature.com/gp/authors/campaigns/latex-author-support. It is highly recommended that submitted papers do not exceed 20 pages including references. Every paper may be accompanied by unlimited appendices.

• All submissions will be blind reviewed, therefore author names and affiliations must not appear in the submissions, and bibliographic references must be adjusted to preserve author anonymity. Submissions failing to comply with paper formatting and authors anonymity may be rejected without review.
• Because of the double-blind review process, non-anonymous papers that have been issued as technical reports or similar cannot be considered for DSAA'2024 Journal Track. An exception to this rule applies to arXiv papers that were published in arXiv at least a month prior to DSAA'2024 Journal Track's submission deadline. Authors can submit these arXiv papers to DSAA provided that the submitted paper's title and abstract are different from the one appearing in arXiv.

All papers will be reviewed following the standard reviewing guidelines of the Journal.

Review and Decision

• All submissions will be reviewed by the journal track's guest editorial board members of the DSAA'2024 Journal Track using rigorous scientific criteria including the basis of technical quality, relevance to the conference's topics of interest, originality, significance, and clarity, where originality and contribution will be crucial.
• Papers acceptable to the journal track will be invited for the journal submission by the corresponding author to the MLJ submission system SNAPP for further revision and review. The MLJ submission should include the reviews and meta-reviews from CMT in its cover letter, the response to the reviews, and a new revision. The final decision will be made by the EIC or the editor for special issues of MLJ.
• Papers rejected by the MLJ but considerable for DSAA'2024 will be transferred to the conference program chairs for their inclusion into the DSAA'2024 program and proceedings.

Important Policies

1. Reproducibility & supplementary: The advancement of data-driven discovery depends heavily on reproducibility. We strongly recommend that the authors release their code and data to the public. Authors can provide an optional supplement at the end of their submitted paper. This supplement can only be used to include (i) information necessary for reproducing the experimental results reported in the paper (e.g., various algorithmic and model parameters and configurations, hyper-parameter search spaces, details related to dataset filtering and train/test splits, software versions, detailed hardware configuration, etc.), and (ii) any data, pseudo-code and proofs that due to space limitations, could not be included in the main manuscript.
2. Authorship: The author list as submitted with the paper is considered final. No changes to this list may be made after paper submission, either during the review period or in case of acceptance, at the final publication stage.
3. Dual submissions: DSAA journal track is an archival publication venue as such submissions that have been previously published, accepted, or are currently under-review at peer-review publication venues (i.e., journals, conferences, workshops with published proceedings, etc.) are not permitted. DSAA journal track has a strict no dual submission policy.
4. Conflicts of interest (COI): COIs must be declared at the time of submission. Declare any conflict of interest by reporting the email domains of all institutions with which the authors have an institutional conflict of interest. Authors have an institutional conflict of interest if they are currently employed or have been employed at this institution in the past three years, or if the authors have extensively collaborated with this institution within the past three years. Authors are also required to identify all track chairs and guest editorial board members if released with whom the authors have a conflict of interest. Examples of conflicts of interest include co-authorship in the last five years, a colleague in the same institution within the last three years, and advisor/student relationships. Journal track chairs are not allowed to submit papers to the journal track. Guest editorial board members may be allowed to submit to the journal track without COI in the review.
5. AI generated text: The use of generative artificial intelligence (AI)-generated text in an article shall be disclosed in the acknowledgements section of any paper submitted to the DSAA journal track. The sections of the paper that use AI-generated text shall have a citation to the AI system used to generate the text.

Paper Presentation at DSAA'2024

Authors submitting their work to the DSAA'2024 Journal Track commit themselves to present their papers at the DSAA'2024 conference if accepted either by MLJ or by DSAA'2024. At least one author of each accepted paper must register in full and attend the conference to present the journal track or conference paper. No-show papers may be removed from the journal track or conference proceedings with their supervisors informed.

Guest Editor

• Longbing Cao, Macquarie University, Australia
• David C. Anastasiu, Santa Clara University, USA
• Qi Zhang, Tongji University, China
• Xiaolin Huang, Shanghai Jiaotong University, China

Contact

For further information and enquiries, please contact Mail: mlj.dsaa2024@gmail.com.

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