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All submission deadlines are end-of-day in the Anywhere on Earth (AoE) time zone.
Papers for this workshop must be written in English and formatted according to the ACM style, using the ACM template (available for both LaTeX and Word users). Papers should follow the double-column format typical of ACM proceedings. (See Submission Guidelines).
Papers may range from 4 to 8 pages in length. Authors may include up to 2 additional pages for references and appendices. There is no distinction between "long" and "short" papers; authors can select the length that best suits their work.
Paper submissions must conform to the “double-blind” review policy, meaning that authors should omit their names and affiliations from the paper. This ensures a fair and unbiased evaluation of all submissions. All papers will be peer-reviewed by experts in the field. Acceptance will be based on relevance to the workshop, scientific novelty, and technical quality.
As part of the submission process, please ensure that your work adheres to ethical standards regarding AI-generated content. Specifically: (i) Authors are required to disclose any use of tools such as ChatGPT or other AI-based systems in the creation of the paper. This includes usage for generating text, analysis, or other content. (ii) If AI tools are used in the paper preparation, authors must ensure that proper attribution is provided and that the tool is used ethically. (iii) AI as authors: AI tools should not be listed as authors. Only human contributors should be credited as authors. Please ensure that all contributors meet the ACM Publications Policies.
To submit your work, kindly use the following Submission Link.
In today’s digital landscape of privacy breaches, misinformation, and algorithmic bias, this workshop fosters cross-domain collaboration, applying successful techniques across fields. By bridging academia and industry, it aims to translate research into actionable solutions for modern web challenges.
We invite submissions on a wide range of topics across five themes, including but not limited to the following areas. For more details, please refer to the attached file.
| Survey Nature |
Overviews of specific fields. State-of-the-art methods and frameworks. Examples: Video techniques: Review advancements in compression and analysis. Data-driven video analytics: Machine learning for content analysis. |
|---|---|
| Evaluation Focus |
Critical analysis of solutions. Identify strengths and weaknesses. Examples: Ethical AI in healthcare: Ensure transparency and accountability. Clinical AI privacy: Review security measures. Video moderation: Assess accuracy and ethics. |
| Methodologies |
Critiques and improvements of methods. Examples: Data annotation: Best practices for AI performance. LLMs in healthcare: Focus on interpretability. Social media video: Analyze motion and engagement. |
| Cross-Disciplinary |
Interdisciplinary discussions. Diverse perspectives on challenges. Examples: Smart cities: AI for urban improvement. Online extremism: Detect and mitigate hate speech. Human-centric analysis: Study motion for security and health. |
| Emerging Challenges |
Address pressing issues. Propose future research. Examples: Misinformation: Combat false data in crises. Trust in data: Ensure quality and reliability. Video quality: Improve assessment methods. |