The WISE2024 Research Track solicits high quality, original papers focusing on important research problems and issues around the Web as a universal platform connecting people, machines and systems. We are particularly interested in submissions focusing on research areas that aim to maximize the positive impact of the Web (and its underlying information systems) on the society and touch the lives of people in important ways.

Topics of interest include but are not limited to:

    Web-scale Distributed and Cloud Computing
    Recommender Systems
    Deep/Hidden Web
    Clickstream Analytics
    Rich Web UI and HCI
    Semantic Web
    Machine Learning for the Web
    Linked Open Data
    Human Factors and Social Issues

    Web Big Data Techniques and Mining
    Social Web Models and Analysis
    Web Agents and Web Intelligence
    Web Data Models
    Web Information Retrieval and Text Analytics
    Web Metrics and Performance
    Web Mining and Web Warehousing
    Web Security and Trust Management
    Web-based Business Processes and Web Services
    Web Tools and Visualization
    Data Science and AI technology
    Web-based Applications (e.g., Auction and Negotiation, e-Commerce, e-Government, e-Learning, etc.)

Special Track: Trustworthy Machine Learning for Web Information Systems (TL4WIS)

https://crowdos.cn/TL4WIS/

The special track “Trustworthy Machine Learning for Web Information Systems” aims to address the pivotal challenges and opportunities that arise at the intersection of trustworthy machine learning and web information systems. As machine learning becomes increasingly integral to web technologies, ensuring these systems are privacy-preserving, robust, secure, fair, and transparent is paramount. This track seeks to bring together a diverse array of research contributions that explore innovative approaches to enhancing the trustworthiness of machine learning based web information systems. We expect to gather researchers from academia and industry to present the latest advances and future directions in designing, deploying secure and trustworthy machine learning algorithms, techniques, and protocols for real-world web applications, services, and systems. In this track, we solicit research papers and position papers to investigate best practices, new methods, and secure design principles. Ultimately, the goal is to advance the state of the art in creating machine learning based web systems that are not only technically proficient but also ethically sound, socially responsible, and trusted by users and stakeholders alike.

Topics: Areas of interest include, but are not limited to:

• Adversarial/poisoning threats against AI/ML based web applications and systems
• Defences to improve deep learning web system robustness
• Privacy-preserving machine learning algorithms and protocols for web applications and information systems
• Privacy inference attacks against AI/ML based web systems, e.g., membership inference, model extraction, model inversion
 • Intersection among fairness, privacy, robustness, explainability, accountability, and environmental wellbeings in AI/ML powered web systems
• Secure federated learning for decentralised and collaborative web applications and systems
• Methodologies for detecting, measuring, and mitigating bias in machine learning models, with a focus on web applications
• Ethical implications of deploying machine learning in web systems, including considerations of user consent, data protection, and societal impacts
• Application of machine learning to detect, prevent, and respond to cyber threats in web systems
• Studies focusing on the user perspective in ML powered web systems, including user studies, trust modeling, and user experience design
• Development of benchmarks, datasets, and evaluation frameworks specifically designed for assessing the trustworthiness of machine learning in web information systems.

Important Dates:
Submission Deadline: 30 June, 2024
Acceptance/Rejection Notification: 30 August, 2024 
Camera-Ready Files Submission Deadline: 07 September, 2024 
Conference date: 2 – 5 December, 2024

Publication

Please note that for every accepted paper, it is required that at least one person registers for the conference and presents the paper. All accepted papers will be included in the conference proceedings published as Springer’s LNCS series.

A selection of accepted papers can be extended and published in prestigious journals including ACM Transaction on the Web, Springer WWW and Springer Computing."