The International Journal of eScience

Computing infrastructures and systems are rapidly developing and so are novel ways to map, control and execute scientific applications which become more and more complex and collaborative.
Computational and storage capabilities, databases, sensors, and people need true collaborative tools. Over the last years there has been a real explosion of new theory and technological progress supporting a better understanding of these wide-area, fully distributed sensing and computing systems. Big Data in all its guises require novel methods and infrastructures to register, analyze and distill meaning.

FGCS aims to lead the way in advances in distributed systems, collaborative environments, high performance and high performance computing, Big Data on such infrastructures as grids, clouds and the Internet of Things (IoT).

The Aims and Scope of FGCS cover new developments in:

[1] Applications and application support:

    Novel applications for novel e-infrastructures
    Complex workflow applications
    Big Data registration, processing and analyses
    Problem solving environments and virtual laboratories
    Semantic and knowledge based systems
    Collaborative infrastructures and virtual organizations
    Methods for high performance and high throughput computing
    Urgent computing
    Scientific, industrial, social and educational implications
    Education

[2] Methods and tools:

    Tools for infrastructure development and monitoring
    Distributed dynamic resource management and scheduling
    Information management
    Protocols and emerging standards
    Methods and tools for internet computing
    Security aspects

[3] Theory:

    Process specification;
    Program and algorithm design
    Theoretical aspects of large scale communication and computation
    Scaling and performance theory
    Protocols and their verification

Special Issue on Next-Generation Web 3.0 for Digitalized Industrial Applications in the 5G/6G Era
Submission Date: 2024-07-15

With the rapid development of 5G/6G communication networks, billions of IoT devices are being connected to the network, thus generating a large amount of data. The collection, processing, and analysis of this vast amount of data are essential to help people and enterprises gain valuable information, make sensible decisions, and improve people’s lives. However, the underlying communication networks also face many new challenges. Managing these large number of devices in a scalable and secure manner brings significant challenges to the infrastructure construction, maintenance, and management of the communication networks. Recurring data privacy breaches and the lack of control make Internet users and enterprises less willing to provide valuable data for processing and analysis.

In the context of the rapidly evolving 5G/6G era, the relevance of Web 3.0 to digital industrial applications is multifaceted and transformative. By emphasizing enhanced security and decentralized data management, Web 3.0 technologies offer robust solutions to safeguard sensitive industrial information and ensure resilient data infrastructure. These technologies enable streamlined supply chain and asset management through transparent and tamper-resistant data exchange, while the integration of smart contracts facilitates automated agreements, expediting operational efficiency. Furthermore, the implementation of self-sovereign identity systems empowers users to manage and authorize data access, ensuring heightened privacy compliance. The compatibility of Web 3.0 with advanced 5G/6G network capabilities enables seamless integration, promoting efficient communication, computation, and data processing within the industrial landscape. Overall, the integration of Web 3.0 technologies fosters a dynamic, secure, and interconnected industrial ecosystem poised for sustainable growth and innovation.

Authors are invited to submit high-quality papers containing original work from either academia or industry reporting novel advances in (but not limited to) the following topics on Web 3.0 for digitalized industrial applications in the 5G/6G era:

Guest Editors
Qingqi PeiXidian University, Chinaqqpei@mail.xidian.edu.cn
F. Richard YuCarleton University, Canadarichard.yu@carleton.ca 
Kaou OtaMuroran Institute of Technology, Japan ota@csse.muroran-it.ac.jp
Mohammed AtiquzzamanUniversity of Oklahoma, USAatiq@ou.edu
Youshui LuXi’an Jiaotong University, Xi’an, Chinayolu6176@uni.sydney.edu.au

Important Dates
Submission portal opens: January 15, 2024
Deadline for paper submission: July 15, 2024
Latest acceptance deadline for all papers: November 15, 2024 

Special Issue on AIFI – Artificial Intelligence for Interoperability
Submission Date: 2024-11-15

Motivation and Scope

With the increasing number of data processing technologies, interoperability represents one of the biggest challenges in gathering information from heterogeneous sources. Data collection involves different hardware and software solutions, the adoption of different formats, and the sharing of different meanings. Data interoperability encompasses how diverse datasets are exchanged, merged or aggregated in seamless and meaningful ways, enabling the extraction of knowledge that can be inferred from the whole dataset but not from single sources. While research in this domain explored diverse strategies, the advent of Artificial Intelligence (AI) represents a new frontier in enhancing data interoperability.

AI can intervene at different steps of the data processing lifecycle: at acquisition time to adapt data collection for future use or compatibility with other technologies, during the storing process to perform semantic enrichment and alignment with a broader dataset, during the data analysis to federate knowledge from multiple repositories.

This special issue explores diverse approaches, challenges, and benefits in AI-driven data interoperability, focusing on the domains of the Internet of Things, Big Data, and Knowledge Graphs. This special issue can raise valuable insights into how AI can enhance data interoperability, enabling efficient use of diverse datasets to drive innovation and ease knowledge discovery.

The topics of this special issue include (but are not limited to):

Middleware Strategies empowered by AI
AI-Driven Standardization and Adaptive Data Formats
Federated Knowledge Extraction
Interoperability solutions based on Generative AI
Interoperable architecture, protocols, and standards for large-scale IoT deployments enabled by AI.
Automatic design and integration of IoT standards, such as the W3C Web of Things (WoT) specifications, powered by AI techniques.
Methods and algorithms to improve interoperability at the knowledge level
AI solutions for blockchain interoperability
Explainable AI for enhanced interoperability
AI-based data privacy and security in interoperable systems
AI-driven ontology alignment and semantic interoperability

Guest Editors

Luca SciulloUniversity of Bologna, Italyluca.sciullo@unibo.it
Ivan ZyrianoffUniversity of Bologna, Italyivandimitry.ribeiro@unibo.it
Ronaldo C. PratiFederal University of ABC, Brazilronaldo.prati@ufabc.edu.br
Lionel MediniUniversity of Claude Bernard Lyon 1, France lionel.medini@liris.cnrs.fr

Important Dates
Submission portal opens: May 15th, 2024
Deadline for paper submission: November 15th, 2024
Latest acceptance deadline for all papers: March 15th, 2025 

Special Issue on Explainable Artificial Intelligence in Drug Discovery and Development
Submission Date: 2024-12-15

Motivation and Scope

'Artificial Intelligence' (AI) has recently revolutionized the field of drug discovery and development, achieving breakthroughs in areas such as molecular design, chemical synthesis planning, protein structure prediction, and macromolecular target identification. Despite various computational methods proposed to address practical challenges, the complexity of these algorithms often results in limited explainability of the models, hindering our ability to understand and explain their underlying mechanisms. Given the rapid advancement of AI in drug discovery and related fields, there is an increasing demand for methods that help us understand and interpret the underlying models. Consequently, proposing 'Explainable Artificial Intelligence' (XAI) methods to address the challenge posed by the lack of explainability in deep learning models and enhancing human reasoning and decision-making capabilities have become imperative.

This special issue aims to gather papers that focus on integrating and applying advanced XAI algorithms to address the most fundamental questions in drug discovery and development, including drug repositioning, potential drug target identification, and small drug molecule target interaction and binding affinity prediction, etc. We expect the articles covering this special issue can effectively promote the drug discovery in methodology and meanwhile provide interesting insights or new biological observations.

The topics of this special issue include but not limited to:

Prediction of drug properties with XAI
Explaining drug-drug/target interaction through XAI
Development of explainable large language models for drug discovery
XAI for drug and target feature representation
XAI for ab initio drug design
XAI for virtual screening drugs

Guest Editors

Leyi WeiShandong University, Chinaweileyi@sdu.edu.cn 
Balachandran ManavalanSungkyunkwan University, South Koreabala2022@skku.edu 
Xiucai YeUniversity of Tsukuba, Japan yexiucai@cs.tsukuba.ac.jp 
Dariusz MrozekSilesian University of Technology, PolandDariusz.Mrozek@polsl.pl 

Important Dates
Submission portal opens: March 20, 2024
Deadline for paper submission: Dec. 15, 2024
Latest acceptance deadline for all papers: March 1, 2025 

Special Issue on On-device Artificial Intelligence solutions with applications on Smart Environments
Submission Date: 2025-02-25

Motivation and Scope

The recent advancements in Artificial Intelligence (AI) and the increasing computational power acted as catalyzer for the widespread diffusion of Intelligent Cyber Physical Systems (ICPSs) as a novel way to run smart applications with a “reasoning” component. Unfortunately, the limited hardware capabilities of these devices pose significant limitations on the complexity of the tasks and Deep Learning models that can be run effectively. During the years, solutions like weights compression or quantization have been proposed to address this issue, but they usually require a careful tuning and most of the time they consist in a post-training operation. From the very beginning, the training of complex Deep Learning models has always been reserved to powerful machines with large computing capabilities (typically identified in the Cloud), limiting the Edge only to the inference. However, these solutions do not work especially when latency, security, and high customization aspects become key prerequisites. In such a context emerges the need of novel methods to deliver the intelligence into an embedded system without the data leaving the device. Originally born as a complementary technology, On-device AI is expected to become a hot topic in the next years as a new paradigm where both training and inference processes are performed on the same device. If on the one hand, the possibility to run intelligent algorithms on these systems is a challenging task, on the other the benefits in terms of response time and energy efficiency derived from this technology are going to be the foundations for a novel type of “reasoning” systems. To this aim novel architectures and frameworks should be explored to enable the access to AI based tailored services. Considering a scenario where the Edge would potentially store sensitive data (that should never travers the Internet), it is evident that these devices could become the target of attacks by malicious users. In this sense, privacy and security aspects represent another key elements to be carefully considered and implemented.

This special issue has the goal to promote original, unpublished, high-quality research about On-device AI solutions applied to the Smart Environments and Industry 4.0 contexts.

The topics of interest include, but are not limited to:

    On-device training solutions
    On-device AI applications
    Federated Learning training and inference strategies on Edge devices
    AI Intelligent Systems
    AI for Microcontrollers
    AI applications at the Edge
    AI methods for Industrial applications
    AI based services at the Edge
    Hardware efficient Deep Learning applications
    Energy efficient Deep Learning algorithms
    Privacy and Security for Deep Learning
    Comparative analysis of on-device AI frameworks
    Implementation case studies
    Low-power AI applications and methods
    Lightweight AI algorithms for Edge devices
    Edge architectures and frameworks for AI

Important Dates

Submission portal opens: July 25, 2024
Deadline for paper submission: February 25, 2025
Latest acceptance deadline for all papers: June 30, 2025