ACM Transactions on Intelligent Systems and Technology (ACM TIST) is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. It is published six times per year.

An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world.

The journal welcomes articles that report on the integration of artificial intelligence technology with various subareas of computer science as well as with other branches of sciences and engineering. The journal welcomes innovative, high-impact articles on emerging or deployed intelligent systems and technology with solid evaluation or evidence of success on a variety of topics. An emerging intelligent system is one that is evaluated on benchmark data and has the potential to become deployed in the future, whereas a deployed system should demonstrate useful case studies and application experiences with sufficient novelty and principled methodology in contribution to the state of the art.

Topics

Topics include, but are not limited to:

    intelligent agent systems
    intelligent bioinformatics and biomedical systems
    intelligent information and multimedia systems
    intelligent robotic systems
    intelligent tutoring and educational systems
    knowledge engineering, modeling and capture systems
    knowledge based software engineering
    large-scale machine learning systems and technology
    machine learning applications
    online commerce, games, information search and advertising systems
    planning and scheduling systems
    social intelligence systems
    speech and language understanding and processing systems
    user behavior modeling and learning, intelligent user interfaces

The journal also welcomes articles on multidisciplinary systems such as brain sciences, social intelligence, and integration of intelligent systems with business, science and engineering applications.  The journal is committed to the timely dissemination of research results in the area of intelligent systems and technology.

Special Issue on Transformers
Día de Entrega: 2024-12-01

Guest Editors:
• Feng Xia, RMIT University, Australia; feng.xia@rmit.edu.au; f.xia@ieee.org
• Tyler Derr, Vanderbilt University, USA; tyler.derr@vanderbilt.edu
• Anh Tuan Luu, Nanyang Technological University, Singapore; anhtuan.luu@ntu.edu.sg
• Richa Singh, IIT Jodhpur, India; richa@iitj.ac.in
• Aline Villavicencio, University of Exeter, United Kingdom; a.villavicencio@exeter.ac.uk

Transformer-based models have emerged as a cornerstone of modern artificial intelligence (AI), reshaping the
landscape of machine learning and driving unprecedented progress in a myriad of tasks. Originating from the
domain of natural language processing, transformers have transcended their initial applications to become
ubiquitous across diverse fields including anomaly detection, computer vision, speech recognition, recommender
systems, question answering, robotics, healthcare, education, and more. The impact of transformer models
extends far beyond their technical intricacies. For instance, advanced transformers have been successfully applied
to multimodal learning tasks, where they can seamlessly integrate information from different modalities such as
text, images, audio, and video. This ability opens up new avenues for research in areas like visual question
answering, image captioning, and video understanding.

Despite their remarkable success, however, several challenges remain. For example, training large transformer
models often requires significant computational resources. Researchers are actively exploring efficient training
methods, such as pre-training on massive datasets and knowledge distillation techniques, to address these
limitations. Additionally, fostering explainability in transformer models is crucial for understanding their decision-
making processes and building trust in real-world applications.

As transformers continue to evolve and permeate various sectors of AI, it becomes increasingly imperative to
explore their advancements and applications comprehensively. This special issue seeks to provide a platform for
researchers to showcase the latest developments, challenges, and opportunities in the field of transformers
across diverse domains, fostering interdisciplinary dialogue and innovation.

Topics

This special issue invites contributions covering a wide range of topics related to advances in transformers. Topics
of interest include, but are not limited to:
• Novel architectures and variations of transformer models
• Theoretical insights into transformers
• Efficient training and deployment of large-scale transformer models
• Fine-tuning strategies for pre-trained transformer models
• Interpretability and explainability of transformers
• Trustworthy, safe, and responsible transformers
• Transformers for diverse machine learning tasks
• Transformers for science
• Transformer-based approaches for multimodal learning
• Transformer foundation models and transformer-based generative AI
• Applications of transformers in various domains such as healthcare, education, robotics, etc.
• Ethical considerations and societal impacts of transformer technology

Important Dates
• Submissions deadline: December 1, 2024
• First-round review decisions: March 1, 2025
• Deadline for revision submissions: April – June 2025
• Notification of final decisions: July 1, 2025
• Tentative publication: September 2025

Submission Information
Submissions must be prepared according to the TIST submission guidelines
(https://dl.acm.org/journal/tist/author-guidelines) and must be submitted via Manuscript Central
(https://mc.manuscriptcentral.com/tist).
For questions and further information, please contact Prof. Feng Xia (feng.xia@rmit.edu.au; f.xia@ieee.org).