“Explanatory Data Science in Technology Applications” is a free call for a J.UCS special issue edited at Graz University of Technology, Austria. 

Prof. Dr. Christian Gütl (c.guetl@tugraz.at); Managing Editor-in-Chief, Johanna Zeisberg, M.A. (johanna.zeisberg@tugraz.at) Publishing Manager; paper selection of the 4th Workshop on Collaborative Technologies and Data Science in Smart City Applications (CODASSCA 2024): Data Science and Reliable Machine Learning held in Yerevan, Armenia, October, 3-6, 2024, https://codassca2024.aua.am/.

Guest Editors for the J.UCS special issue

Dr. Han Vinck, Senior professor in Digital Communications at the University of Duisburg-Essen, 47057 Duisburg, Germany, han.vinck@uni-due.de     https://www.uni-due.de/dc/vinck-main.php https://ieeexplore.ieee.org/author/37269024500 

https://scholar.google.de/citations?user=Lt_qg1kAAAAJ&hl=de  

Dr. Wolfram Luther, Senior Professor of Computer Science, Computer Graphics and Scientific Computing, University of Duisburg-Essen, 47057 Duisburg, Germany, wolfram.luther@uni-due.de

https://orcid.org/0000-0002-1245-7628

https://scholar.google.com/citations?user=ttNYj5wAAAAJ&hl=en

Since the special issue includes extended versions originally presented at the workshop, a public call for papers will also be made, and the issue will contain at least two papers that originate from the public call for papers. The extended version of papers originally presented at the conference must contain about 50% new material, and the title of the extended version must clearly and unmistakably differ from the title of the article presented at the conference. The maximal length of a paper formatted according to the J.UCS guidelines may not exceed 25 pages.

Rationale

Data from all areas of daily life that are increasingly accessible to a broad public enable the conception, creation, calibration, and validation of a process or complex systems. However, this requires international standards for data quality and access, system design and implementation, and human-centered computing technologies. 

Data volumes are growing rapidly due to environmental sensor reading, sensor networks, broad-band services, multimodal communication, whereas pervasive and embedded computing is enhancing the capabilities of everyday objects and easing collaboration among people.

Mobile systems could enhance the possibilities available for designers and practitioners. Effective analysis, quality assessment and utilization of big data is a key factor for success in many business and service domains, including the domain of smart systems. Major industrial domains are on the way to perform this tectonic shift based on Big Data, Artificial Intelligence (AI), Collaborative Technologies, Smart Environments supporting Virtual and Mixed Reality Applications, Multimodal Interaction, and Reliable Visual and Cognitive Analytics.

However, many requirements still need to be met, and complex problems solved before information and knowledge can be generated effectively and efficiently from the huge amount of data. The first one is to ensure data quality, which includes accuracy and integrity of the obtained data, timely delivery, suitable quantity, etc. Privacy and security requirements and thorough end‐to‐end rights complement realization and deployment of modern design, software development and evaluation tools. The second one is to create explainable and understandable models, which can turn data into valuable information and then into knowledge. 

A general challenge is the low interpretability of various AI approaches and ML models, for which it is important that data scientists design the models, users understand results, and developers debug and improve the tools. The increasing complexity, limited explainability and interpretability of the complex ML models make it difficult to address the emerging requirements for acceptance of these models and hinders their applications in industrial and mission-critical scenarios. 

Therefore, explainability, interpretability, transparency, and accountability of ML models and systems need to be further developed for an effective use of AI technologies. They are a prerequisite for a reliable application of AI within many problem areas, e.g., natural language processing, risk prediction in healthcare, fault/anomaly detection, computer vision or classification and regression under uncertainty, which are significant ML tasks. Researchers and practitioners working on theoretical and practical aspects of data science and reliable machine learning, as well as related and fundamental topics of information theoretic approaches for smart systems and computer system organization, were invited to contribute to. The editors plan to invite five author groups from Armenia, Chile, Germany, Great Britain, and the USA to submit an extended version, which will be subjected to a standardized preliminary review and evaluated in a second round by at least two additional reviewers. 

Topics of interest include

- Computer System Organization

- Data Science and Information Theoretic Approaches for Smart Systems

- Technical Challenges for Smart Environments

- Explainable Artificial Intelligence, Neural Networks and Deep Learning

- Smart Human-Centered Computing

Topics and challenges in theoretical foundations are—and this list is by no means exhaustive—the following: tight error bounds, adequate, explainable models, metrics or risk estimations, ill‐conditioned or too complex problems, inefficient algorithms, unknown, insensitive or parameters with uncertainty; probabilistic models with outliers, wrong sensor readings, or incomplete result evaluation, unfair and discriminatory decision systems.

Practical applications are use cases which realize relevant parts of emerging computing technologies, its “self‐ driving” capabilities employing data analytics, a data quality audit and analysis of validity and reliability of results using quality criteria and metrics, and considering overarching principles. Links to architectures and implementations on which the application results are based are expressly encouraged.  

Further regulations and information for J.UCS Special Issues authors and guest editors

The extended version of papers originally presented at a conference or workshop must present substantially new material, and the title of the extended version must clearly and unmistakably differ from the title of the article already presented at a conference/workshop. A special issue may contain a maximum of 8 papers, at least 2 from the free call. The length of a paper should be between 20-25 pages and must comply with the JUCS style guidelines for authors without exception. All accepted articles will be subjected to a plagiarism check before publication.

To secure and increase the impact factor of the journal, it is expected that the authors consult J.UCS before the publication of their articles and quote relevant J.UCS publications in their articles. This measure is in the interest of the authors, since it is a contribution to the reputation of the journal. 

Important Deadlines for 2025 J.UCS Special Issue Explanatory Data Science in Technology Applications

Authors must follow the J.UCS Special Issues guidelines, the requirements and instructions of the reviewers; the papers should be submitted via Easychair https://easychair.org/conferences/?conf=jucscodassca2025