Intelligent Computing: A Science Partner Journal

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Intelligent Computing: A Science Partner Journal

An Open Access journal published in affiliation with Zhejiang Lab and distributed by AAAS.

07/11/2025

“Critical concerns regarding the security and privacy of information transmitted within Internet of Medical Things systems have increased greatly, since these systems manage and generate substantial amounts of sensitive private data. Current traditional security methods have not yet adapted to evolving cyber threats, making the need for data security in medical settings crucial.”

https://www.eurekalert.org/news-releases/1103972

News release highlighting key points in “Privacy-Preserving strategies in the Internet of Medical Things Using Reinforcement Learning and Blockchain”

06/11/2025

“We propose a security framework based on blockchain technology and distributed reinforcement learning. A decentralized cognitive blockchain network is utilized to ensure that data are stored securely and transmitted reliably while minimizing resource utilization. Distributed reinforcement learning is integrated to enable security measures to adapt to changing threat patterns and enhance system resilience.”

https://spj.science.org/doi/abs/10.34133/icomputing.0133

Peer-reviewed open access research article: “Privacy-Preserving strategies in the Internet of Medical Things Using Reinforcement Learning and Blockchain”

05/11/2025

“The deep reinforcement learning part of the method is used to predict vectorization factors and inject the vectorization pragmas with the optimal vectorization factor and interleaving factor into the code to achieve better performance. The new framework guides the compiler with smarter choices than either the default baseline O3 or the earlier machine learning method NeuroVectorizer.”

https://www.eurekalert.org/news-releases/1103977

News release highlighting key points in “A Graph-Based Learning Framework for Compiler Loop Auto-Vectorization”

04/11/2025

“Loop-based vectorization targets loops and optimizes their performance by grouping multiple occurrences of the same operation across loop iterations into a single instruction. We propose a data-driven graph-based learning framework for automatic vectorization which takes an input program, extracts the loops, and then learns a structured representation to automatically predict the vectorization and interleaving factors.”

https://spj.science.org/doi/abs/10.34133/icomputing.0113

Peer-reviewed open access research article: “A Graph-Based Learning Framework for Compiler Loop Auto-Vectorization”

03/11/2025

Special Issue Call for Papers!

Intelligent Computing for Finance and Social Systems: From Large Models to Trustworthy AI Applications

Submission deadline: May 11, 2026

This special issue aims to bring together cutting-edge research that explores the theory, methods, and applications of AI-driven innovations in the domains of finance and social computing. We welcome contributions that address novel AI algorithms, interdisciplinary methodologies, and practical systems that facilitate trustworthy, efficient, and socially responsible decision-making in financial and social contexts.

This special issue solicits original research articles, experimental articles, and review articles, as well as benchmark datasets and open-source platforms.

Topics of interest include, but are not limited to:
>> Financial data mining and predictive analytics
>> AI-driven risk management and portfolio optimization
>> AI-based fraud detection and anti-money laundering techniques
>> Large language models and generative AI for financial and social analysis
>> Explainable and trustworthy AI for financial decision-making
>> Financial network analysis and systemic risk detection
>> Privacy-preserving AI in social data analysis
>> Multi-agent systems for financial and social simulations
>> AI ethics, fairness, and transparency in finance and social computing
>> AI for economic and social impact modeling
>> Social network and Sentiment analysis empowered by AI
>> Human-AI collaboration in social computing environments

Guest Editors:
>> Xiang Ao, Institute of Computing Technology, Chinese Academy of Sciences, China
>> Dawei Cheng, Tongji University, China
>> Yue Zhang, Westlake University, China
>> Derek F. Wong, University of Macau, China
>> Muhammad Aamir Cheema, Monash University, Australia

https://spj.science.org/page/icomputing/si/intelligent-computing-finance-social-systems

15/10/2025

“Biomateriomics has biomedical applications such as tissue engineering, regenerative and personalized medicine, drug delivery, and drug discovery. However, exploring the huge design space can be labor-intensive and expensive. Generative artificial intelligence methods are seen as a way to conduct biomateriomics research that would otherwise be impracticable.”

https://www.eurekalert.org/news-releases/1101465

News release highlighting key points in “Generative Artificial Intelligence for Advancing Discovery and Design in Biomateriomics”

14/10/2025

“We examine how generative AI techniques are revolutionizing the discovery, design, property prediction, and optimization of biomaterials across multiple scales and applications, particularly in tissue engineering, regenerative medicine, and drug discovery. Furthermore, we discuss the synergies between generative AI and other cutting-edge technologies

https://spj.science.org/doi/10.34133/icomputing.0117

Peer-reviewed open access review article: “Generative Artificial Intelligence for Advancing Discovery and Design in Biomateriomics”

08/10/2025

“The method developed in this research involves encoding the variational parameters of neural networks into tensor networks using a 'brick-wall' structure. Each block represents a tensor, and the bonds connected to a block represent the indices of the corresponding tensors. In the new method, there are 2 main stages to obtain tensors.”

https://www.eurekalert.org/news-releases/1099328

News release highlighting key points in “Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters”

07/10/2025

“The superior compression performance of our scheme is demonstrated on several widely recognized NNs (FC-2, LeNet-5, AlexNet, ZFNet, and VGG-16) and datasets (MNIST, CIFAR-10, and CIFAR-100). For instance, we compress 2 linear layers in VGG-16 with approximately 107 parameters to 2 ADTNs with only 424 parameters, improving the testing accuracy on CIFAR-10 from 90.17% to 91.74%.”

https://spj.science.org/doi/abs/10.34133/icomputing.0123

Peer-reviewed open access research article: “Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters”

03/10/2025

“The new regional explanation method bridges the gap between local and global explanations, capturing nonlinear relationships between molecular features and properties. The authors found that different molecular representations showed consistency in their regional explanations. The new method offers fine-grained, chemically meaningful insights often missed by traditional explanation methods.”

https://www.eurekalert.org/news-releases/1093368

News release highlighting key points in “Regional Explanations and Diverse Molecular Representations in Cheminformatics: A Comparative Study”

02/10/2025

“This approach systematically reveals how explanations and feature importance vary across data clusters. Using 2 public datasets, a graphene oxide nanoflakes dataset and QM9, with natural clustering properties, we comprehensively evaluate 4 molecular representations through tabular, sequence, image, and graph regional explanation, providing practical guidelines for representation selection.”

https://spj.science.org/doi/abs/10.34133/icomputing.0126

Peer-reviewed open access research article: “Regional Explanations and Diverse Molecular Representations in Cheminformatics: A Comparative Study”

01/10/2025

“Reinforcement learning enables agents to learn optimal behaviors through real-time interaction with dynamic environment but often suffers from slow convergence and low learning efficiency. Integrating an action curiosity module allows intelligent agents—in this study, robots—to learn more efficiently and obtain rewards while satisfying their curiosity through extensive exploration.”

https://www.eurekalert.org/news-releases/1093605

News release highlighting key points in “Action-Curiosity-Based Deep Reinforcement Learning Algorithm for Path Planning in a Nondeterministic Environment”

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Zhejiang Lab
Hangzhou

Website

https://spj.science.org/journal/icomputing

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