Intelligent Computing: A Science Partner Journal

Intelligent Computing: A Science Partner Journal An Open Access journal published in affiliation with Zhejiang Lab and distributed by AAAS.

“Despite rapid progress, practical quantum walk computing faces challenges, including devising effective algorithms, sca...
03/01/2025

“Despite rapid progress, practical quantum walk computing faces challenges, including devising effective algorithms, scaling up the physical implementations and implementing quantum walks with error correction or fault tolerance. These challenges, however, provide a roadmap for future innovations and advancements in the field.”

https://www.eurekalert.org/news-releases/1068898


News release highlighting key points in “Quantum Walk Computing: Theory, Implementation, and Application”

“Important advances in both scale and programmability are demonstrated for quantum walk simulations and applications—mos...
02/01/2025

“Important advances in both scale and programmability are demonstrated for quantum walk simulations and applications—most of which target problems of practical interest. This suggests that quantum walk models show a feasible path for building a practical and useful NISQ computer in the near future.”

https://spj.science.org/doi/abs/10.34133/icomputing.0097


Open access review article: “Quantum Walk Computing: Theory, Implementation, and Application”

“Looking ahead, the paper calls for Turing-like AI testing that would introduce machine adversaries and statistical prot...
01/01/2025

“Looking ahead, the paper calls for Turing-like AI testing that would introduce machine adversaries and statistical protocols to address emerging challenges such as data contamination and poisoning. These more rigorous evaluation methods will ensure AI systems are tested in ways that reflect real-world complexities, aligning with Turing’s vision of sustainable and ethically guided machine intelligence.”

https://www.eurekalert.org/news-releases/1068915


News release highlighting key points in “Passed the Turing Test: Living in Turing Futures”

“We are now living in one of many possible Turing futures where machines can pass for what they are not. However, the le...
31/12/2024

“We are now living in one of many possible Turing futures where machines can pass for what they are not. However, the learning machines that Turing imagined would pass his imitation tests were machines inspired by the natural development of the low-energy human cortex. They would be raised like human children and naturally learn the ability to deceive an observer. ”

https://spj.science.org/doi/abs/10.34133/icomputing.0102


Open access perspective article: “Passed the Turing Test: Living in Turing Futures”

Intelligent Computing has been accepted by the Web of Science! Articles will be included in the Emerging Sources Citatio...
23/12/2024

Intelligent Computing has been accepted by the Web of Science! Articles will be included in the Emerging Sources Citation Index. The journal will receive an impact factor in 2025. Earlier this year, Intelligent Computing was accepted by Scopus. The journal is already included in the Directory of Open Access Journals (DOAJ).

“The results show that Parkinson's patients displayed specific emotional perception patterns, comprehending emotional ar...
19/12/2024

“The results show that Parkinson's patients displayed specific emotional perception patterns, comprehending emotional arousal better than emotional valence, which means they are more attuned to the intensity of emotions rather than the pleasantness or unpleasantness of those emotions.”

https://www.eurekalert.org/news-releases/1068189


News release highlighting key points in “Exploring Electroencephalography-Based Affective Analysis and Detection of Parkinson’s Disease”

“Our key finding is that both emotion and PD recognition can be reliably performed from EEG responses passively compiled...
19/12/2024

“Our key finding is that both emotion and PD recognition can be reliably performed from EEG responses passively compiled during audiovisual stimulus viewing. Given that we effortlessly interact with media routinely, EEG signals can be captured easily over longer time intervals as compared to resting-state EEG, which can practically be acquired only over short episodes.“

https://spj.science.org/doi/full/10.34133/icomputing.0084


Open access research article: “Exploring Electroencephalography-Based Affective Analysis and Detection of Parkinson’s Disease”

“The method developed by Valantinas and Vettenburg is based on the convergent Born series method for efficient numerical...
18/12/2024

“The method developed by Valantinas and Vettenburg is based on the convergent Born series method for efficient numerical calculations of the wave equation, but can compute wave scattering in a volume 655 times larger than had been achieved previously.”

https://www.eurekalert.org/news-releases/1067662


News release highlighting key points in “Scaling Up Wave Calculations with a Scattering Network”

“Here, we show how coherent scattering calculations can be scaled up to 21 × 106 cubic wavelengths by mapping the physic...
17/12/2024

“Here, we show how coherent scattering calculations can be scaled up to 21 × 106 cubic wavelengths by mapping the physics of multiple scattering onto a deterministic neural network that efficiently harnesses publicly available machine learning infrastructure. We refer to this as a scattering network. “

https://spj.science.org/doi/abs/10.34133/icomputing.0098


Open access research article: “Scaling Up Wave Calculations with a Scattering Network”

“This method for the observation of topological transition in a controlled Kerr nonlinear oscillator system is aligned w...
11/12/2024

“This method for the observation of topological transition in a controlled Kerr nonlinear oscillator system is aligned with the concept of quantum simulation first proposed by physicist Richard Feynman in his 1982 article ‘Simulating Physics with Computers.’ Previous studies observed topological transformations by means of the first Chern number and Berry curvature, but did not use a Kerr nonlinear oscillator.”

https://www.eurekalert.org/news-releases/1067010


News release highlighting key points in “Topological Transitions in a Kerr Nonlinear Oscillator”

“Topological transitions are revealed by the jump of the first Chern number, obtained respectively from the integral of ...
10/12/2024

“Topological transitions are revealed by the jump of the first Chern number, obtained respectively from the integral of the Berry curvature and of the new polar angle relation, over the whole parameter space. Our strategy paves the way for measuring topological transitions in continuous variable systems.”

https://spj.science.org/doi/abs/10.34133/icomputing.0099


Open access research article: “Topological Transitions in a Kerr Nonlinear Oscillator”

Special Issue Call for Papers: Intelligent Computing–Based Time Series Analysis for Cybersecurity Submission deadline: ...
02/12/2024

Special Issue Call for Papers: Intelligent Computing–Based Time Series Analysis for Cybersecurity
Submission deadline: June 3, 2025.

The increasing sophistication of cyber threats has necessitated the development of advanced techniques for analysis and . Intelligent computing-based has emerged as a powerful tool for identifying patterns, anomalies, and trends in security-related data. By leveraging , , and other intelligent computing techniques on time-stamped data, organizations can defend against evolving cyber threats and mitigate risks. This special issue aims to bring together cutting-edge research on the application of intelligent computing-based time series analysis in cybersecurity.

Topics of Interest
This special issue solicits original research articles, experimental and review articles, and database/software articles. Topics of interest include, but are not limited to:

Machine learning and deep learning approaches for time series analysis in cybersecurity
Anomaly detection and threat prediction using time series data
Incident response and forensic analysis through time series techniques
Behavioral analysis and user profiling for cybersecurity
Real-time monitoring and alerting systems based on time series data
Integration of threat intelligence with time series analysis
Case studies, applications, and practical implementations of intelligent computing in cybersecurity
Privacy-preserving techniques for time series analysis in cybersecurity
Explainable AI for interpretable time series analysis in cybersecurity
Blockchain technology for secure time series analysis in cybersecurity
Scalable distributed computing for real-time time series analysis
Human-centric design of intelligent computing systems in cybersecurity

Guest Editors
Le Sun, Nanjing University of Information Science and Technology, China
Deepak Gupta, Maharaja Agrasen Institute of Technology, India
Yudong Zhang, University of Leicester, UK

https://spj.science.org/page/icomputing/si/time-series-analysis-cybersecurity

“The exceptional model mining method of data analysis, which looks for “exceptions” in data patterns, showed not only w...
27/11/2024

“The exceptional model mining method of data analysis, which looks for “exceptions” in data patterns, showed not only which specific brain areas responded to pleasant and unpleasant smells, it also allowed the analysis of differences in brain response between individuals and between subgroups split according to age and sex.”

https://www.eurekalert.org/news-releases/1065688


News release highlighting key points in “Using Exceptional Attributed Subgraph Mining to Explore Interindividual Variability in Odor Pleasantness Processing in the Piriform Cortex and Amygdala”

“A total of 42 volunteers participated in a functional magnetic resonance imaging (fMRI) study in which they were asked ...
26/11/2024

“A total of 42 volunteers participated in a functional magnetic resonance imaging (fMRI) study in which they were asked to smell 6 odors and describe their hedonic valence. Classical univariate analyses (statistical parametric mapping) and data mining were performed on the fMRI data. The results from both analyses showed that unpleasant odors preferentially activate the anterior part of the left piriform cortex.”

https://spj.science.org/doi/abs/10.34133/icomputing.0086


Open access research article: “Using Exceptional Attributed Subgraph Mining to Explore Interindividual Variability in Odor Pleasantness Processing in the Piriform Cortex and Amygdala”

Special Issue Call for Papers: AI for Materials Computing Updated submission deadline: January 31, 2025.The main focus ...
25/11/2024

Special Issue Call for Papers: AI for Materials Computing
Updated submission deadline: January 31, 2025.

The main focus of is to study the complex relationship of “composition-process-structure-property” of materials. In the advent of the digital revolution, artificial intelligence (AI) has emerged as a powerful tool to accelerate the development of new materials and significantly reduce materials development costs. This special issue highlights the recent progress of novel AI-enhanced computational approaches that advance the state-of-the-art in property prediction, process optimization, and inverse design of new materials.

Topics of Interest
This special issue solicits original research, review articles, and commentary articles. Topics of interest include, but are not limited to:

potentials for materials science
Density functional theory with machine learning
Quantum chemistry methods with machine learning
Quantum and classical dynamics with machine learning
Quantum Monte Carlo with machine learning
Phase field with machine learning
Finite element method with machine learning
Materials property prediction with machine learning
Inverse design of new materials with machine learning
Foundation Models/ for materials science

Guest Editors
Prof. Yanjing Su, University of Science and Technology Beijing
Prof. Xiao He, East China Normal University
Prof. Naihua Miao, Beihang University
Prof. Yunhao Lu, Zhejiang University
Prof. Pavlo O. Dral, Xiamen University
Dr. Lipeng Chen, Zhejiang Lab

https://spj.science.org/page/icomputing/si/ai-materials-computing

“Affective computing uses various signals alongside physiological signals and wearable sensors to analyze and synthesize...
22/11/2024

“Affective computing uses various signals alongside physiological signals and wearable sensors to analyze and synthesize affect. While deep learning has significantly improved tasks like emotion recognition through innovations in transfer learning, self-supervised learning and transformer architectures, it also presents challenges, including poor generalization, cultural adaptability issues and a lack of interpretability.”

https://www.eurekalert.org/news-releases/1064164


New release highlighting key points in “Beyond Deep Learning: Charting the Next Frontiers of Affective Computing”

“The success of DL has opened up new paths toward making human–computer interaction more affective. Indeed, the overwhel...
21/11/2024

“The success of DL has opened up new paths toward making human–computer interaction more affective. Indeed, the overwhelming effectiveness of DL has caused many to claim that it is enough to achieve the goals of the community. While this may well prove true, it is important not to overlook additional complementary research directions.”

https://spj.science.org/doi/abs/10.34133/icomputing.0089


Open access review article: “Beyond Deep Learning: Charting the Next Frontiers of Affective Computing”

“The reduction in circuit depth is critical for achieving practical quantum machine learning on current devices, which a...
20/11/2024

“The reduction in circuit depth is critical for achieving practical quantum machine learning on current devices, which are often limited by gate fidelity and qubit count. The increased robustness of these models to adversarial attacks opens up new possibilities for secure quantum machine learning applications in sectors where resilience to tampering is essential.”

https://www.eurekalert.org/news-releases/1064178


News release highlighting key points in “Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning”

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Zhejiang Lab
Hangzhou

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