IEEE Circuits and Systems Society Newsletter | Volume 17 | Issue 2 | April 2023 | CURRENT/PAST ISSUES

PUBLICATION NEWS


Call for Papers - TCSVT Special Issue on AI-Generated Content for Multimedia

With the rapid development of deep learning technology, artificial intelligence-generated content (AIGC) has emerged as a popular area of research in multimedia signal processing, computer vision, and machine learning, with many potential applications, such as dialog generation, text-to-speech conversion, image generation, video generation, and cross-modal generation between audio, video and text. As representative examples of AIGC technology, large-scale models such as ChatGPT, DALL-E and AudioLDM have attracted great attention in their respective fields, which leverage the success of a variety of methods, such as generating adversarial network (GAN), diffusion model, pre-training, and other machine learning approaches. Facilitated by AI algorithms, AIGC can be used to create personalized and unique content at scale, which can be particularly useful in industries such as entertainment, marketing, advertising, transportation, digital media, virtual and augmented reality, among many others. In addition, AIGC can improve accessibility and inclusivity in content creation, making it easier for individuals with disabilities to access and engage with content. It is undeniable that AIGC is gradually changing our lives.

However, the quality of AIGC still needs to be improved, which is not yet at the same level as humans in several fields, such as music generation. Meanwhile, the emergence of AIGC also raises important ethical and legal issues. The ownership of content created by AI algorithms, the applicability of copyright laws to AIGC, and the need to prevent bias or discrimination in AIGC are important issues that need to be addressed. Moreover, how to identify whether the content is created by AI is an urgent problem to be solved.

This special issue aims at exploring the implications of AIGC in various industries and applications. We welcome submissions that examine the technical challenges and opportunities of AIGC, as well as the ethical and legal implications of using this technology. We also encourage submissions that explore the potential of AIGC for improving accessibility and inclusivity in content creation.

Scope

This special issue seeks original contributions from, but not limited to, the following topics:

  • Technical advances in AIGC, including image generation, video generation, audio-visual learning, and other multimedia algorithms.
  • Applications of AIGC in various industries and applications, such as multimedia marketing, advertising, journalism, entertainment, and transportation.
  • The forgery detection and quality evaluation of AIGC, such as fake facial image detection and deep fake video detection.
  • The ethical and legal implications of using AIGC, including issues of ownership, authorship, and accountability.
  • The potential of AIGC for improving accessibility and inclusivity in image, video, and multimedia content creation.
  • The technical challenges and opportunities of AIGC, including issues related to data privacy, bias, and explainability.
  • The role of AIGC in shaping public opinion and influencing decision-making, such as celebrity fake video generation.
  • The future of AIGC and its potential impact on society.

Important Dates:

  • Open for submissions: 6 March 2023
  • Submissions due: 1 June 2023
  • Preliminary notification: 10 August 2023
  • Revisions due: 1 September 2023
  • Notification: 1 October 2023
  • Final manuscripts due: 1 November 2023
  • Publication (tentative): 30 December 2023

Guest Editors:

Dr. Shengxi Li, Professor, Beihang University, China
Dr. Xuelong Li, Professor, Northwestern Polytechnical University, China
Dr. Leonardo Chiariglione, CEDEO.net, Italy
Dr. Jiebo Luo, Professor, University of Rochester, USA
Dr. Wenwu Wang, Professor, University of Surrey, UK
Dr. Zhengyuan Yang, Senior Researcher, Microsoft, USA
Dr. Danilo Mandic, Professor, Imperial College, London, UK
Dr. Hamido Fujita, Professor, Iwate Prefectural University, Japan


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CAll for Papers: JETCAS Special Issue on Dynamical Neuro-AI Learning Systems: Devices, Circuits, Architectures, and Algorithms

Scope and Purpose

The growing demand for artificial intelligence (AI) has spurred the development of: (i) systems that colocate computation and memory, (ii) circuits and devices optimized for operations prevalent in deep learning, and (iii) lightweight and compressed machine learning models that aim to achieve greater performance with less resources.

More experimental and exploratory approaches to optimizing machine learning harness dynamical devices, circuits and systems. Dynamical systems are a natural fit for modeling real-world phenomena as the world is full of complex, higher-order dynamical systems. Neural network algorithms rely on heavily abstracted models of synapses, neurons, and learning rules, where biological details that may be required for brain-level efficiency are stripped away. Integrating the dynamical behaviors present in the brain has the potential to drive accelerators towards a new level of efficiency.

This IEEE JETCAS special issue seeks to draw attention to how the brain and its constituent components behave as a dynamical system to offer software and hardware benefits in modern AI. Software and hardware cannot be decoupled, as in the brain, the neural substrate is a physical manifestation of a neural algorithm. We aim to attract high-quality research papers that tackle the challenging question of how neural dynamics give rise to ultra-efficient, low-power cognition.

While the dominant trend in deep learning follows gradient-based optimization, we aim to complement this by collating a deeper understanding of how higher-order functions, such as problem-solving, decision-making, prediction, planning, attention, memory consolidation, and working memory, can be obtained from lower-level neural dynamics. Developing an understanding can only be achieved by crossing the stacks from low-level device research, the circuits and architectures they create, to higher-level algorithmic abstractions.

We invite neuromorphic and neuro-AI research that uses the power of dynamical systems to promote low-power, brain-inspired learning. This is a fundamentally cross-stack question, which may integrate devices or circuits with architectures or algorithms. By exploring the principles of dynamical systems, neuromorphic computing, neuro-AI, and the various stacks they span, it may be possible to create new and more powerful computing systems.

Topics of Interest

This special issue of IEEE JETCAS will explore academic and industrial research on topics related to dynamical algorithms, architectures, circuits, and devices as applied to neuromorphic systems and neuro-AI. Topics include, but are not limited to:

  • Dynamical Neuromorphic and Neuro-AI Models
    • Dynamical Neural Fields, Dendritic Computation, Columnar Neural Networks, Stochastic Synapses, Reservoir Computing, Oscillatory Networks, Spiking Neural Units, Higher-order complexity, etc. 
  • Neuromorphic Hardware
    • Analog/Mixed-signal neuromorphic circuits, digital accelerators, and neural circuits, near/inmemory computation, physics-driven hardware, and nanowire networks
  • Dynamical Circuits, Devices, and Integration
  • Neuromorphic Algorithms
    • Local learning and bio-plausible learning, evolutionary algorithms, gradient-based learning, continual learning, etc.
  • Neuromorphic Architectures
    • Multiprocessing and parallelism, dataflow and spike routing/collision management, model mapping, etc.
  • Neuromorphic and Neuro-AI systems and tools

Submission Procedure

Prospective authors are invited to submit their papers following the instructions provided on the JETCAS website: https://mc.manuscriptcentral.com/jetcas.

The submitted manuscripts should not have been previously published nor should they be currently under consideration for publication elsewhere.

Important Dates

  • 31 May 2023: Manuscript submissions due
  • 7 August 2023: First round of reviews completed
  • 14 September 2023: Revised manuscripts due
  • 7 October 2023: Second round of reviews completed
  • 21 October 2023: Final manuscripts due

Guest Editors
Jason K. Eshraghian, University of California, Santa Cruz, USA (Corresponding Guest Editor)
Arindam Basu, City University of Hong Kong, Hong Kong
Corey Lammie, IBM ResearchZurich, Switzerland
Shih-Chii Liu, University of Zurich & ETH Zurich, Switzerland
Priyadarshini Panda, Yale University, USA


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