Journal Information
Future Generation Computer Systems (FGCS)
http://www.journals.elsevier.com/future-generation-computer-systems/
Impact Factor:
5.768
Publisher:
Elsevier
ISSN:
0167-739X
Viewed:
32810
Tracked:
122

Call For Papers
The Grid is a rapidly developing computing structure that allows components of our information technology infrastructure, computational capabilities, databases, sensors, and people to be shared flexibly as true collaborative tools. Over the last 3 years there has been a real explosion of new theory and technological progress supporting a better understanding of these wide-area, fully distributed computing systems. After the advances made in distributed system design, collaborative environments, high performance computing and high throughput computing, the Grid is the logical next step.

The new Aims and Scope of FGCS will cover new developments in:

[1] Grid Applications and application support:

    Novel applications
    eScience and eBusiness applications
    Problem solving environments and virtual laboratories
    Grid economy
    Semantic and knowledge based grids
    Collaborative Grids and virtual organizations
    High Performance and high throughput computing on grids
    Complex application workflows
    Scientific, industrial and social implications
    Grids in education

[2] Grid methods and middleware:

    Tools for grid development: monitoring and scheduling
    Distributed dynamic resource management
    Grid- and web-services
    Information management
    Protocols and emerging standards
    Peer to peer and internet computing
    Pervasive computing
    Grid Security

[3] Grid Theory:

    Process specification; program and algorithm design
    Theoretical aspects of wide area communication and computation
    Scaling and performance theory
    Protocol verification


Last updated by Dou Sun in 2019-11-24
Special Issues
Special Issue on Graph-Powered Machine Learning in Future-Generation Computing Systems
Submission Date: 2020-07-15

Recent years have witnessed a dramatic increase of graph applications due to advancements in information and communication technologies. In a variety of applications, such as social networks, communication networks, internet of things (IOTs), and human disease networks, graph data contains rich information and exhibits diverse characteristics. Specifically, graph data may come with the node or edge attributes showing the property of an entity or a connection, arise with signed or unsigned edges indicating the positive or negative relationships, form homogenous or heterogeneous information networks modeling different scenarios and settings. Furthermore, in these applications, the graph data is evolving and expanding more and more dynamically. The diverse, dynamic, and large-scale nature of graph data requires different data mining techniques and advanced machine learning methods. Meanwhile, the computing system evolves rapidly and becomes large-scale, collaborative and distributed, with many computing principles proposed such as cloud computing, edge computing and federated learning. Learning from big graph data in future-generation computing systems considers the effectiveness of graph learning, scalability of large-scale computing, privacy preserving under the federated computing setting with multi-source graphs, and graph dynamics. Today’s researchers have realized that novel graph learning theory, big graph specific platforms, and advanced graph processing techniques are needed. Therefore, a set of research topics such as distributed graph computing, graph stream learning, and graph embedding techniques have emerged, and applications such as graph-based anomaly detection, social recommendation, social influence analytics are becoming important issues. Topics of Interest The topics of interest include, but are not limited to: · Feature Selection for Graph Data · Distributed Computing on Big Graphs · Dynamic and Streaming Graph Learning · Graph Classification, Clustering, Link Prediction · Graph Embedding · Learning from Unattributed/Attributed Networks · Learning from Unsigned/Signed Networks · Learning from Homogenous/Heterogeneous Information Networks · Anomaly Detection in Graph Data · Sentiment Analysis · Cyberbullying Detection in Social Networks · Deep Learning for Graphs · Graph Based Machine Learning · Relational Data Analytics · Social Recommendation · Knowledge Graph Representation Learning · Reasoning over Large-scale Knowledge Bases · Temporal Knowledge Graphs · Federated Learning with Distributed Graphs · Social Computing · Applications of Big Graph Learning
Last updated by Dou Sun in 2020-04-10
Special Issue on Artificial Intelligence: The Security & Privacy Opportunities and Challenges for Emerging Applications
Submission Date: 2020-11-30

In recent years, the collection, processing, and analysis of personal data have become greatly convenient andwidespread, as the continuous advancement of emerging applications such as social networks, Internet of Things (IoT), and cloud computing. This also make sensitive information more vulnerable to abuses, and thus secure mechanisms and technologies tailored for emerging applications need to be explored urgently. Artificial Intelligence (AI) with the benefits of enhancing efficiency and improving accuracy has been widely used in academia and industry. From a privacy and security angle, AI brings about both opportunities and challenges for emerging applications. On the one hand, AI can help interested parties to better protect privacy in challenging situations, improving the state-of-the-art of security solutions. On the other hand, AI also presents risks of opaque decision making, biased algorithms, and safety vulnerabilities, challenging traditional notions of privacy protection. About the Topics of Interest Any topic related to security and privacy aspects in AI and AI-enabled emerging applications with security and privacy will be considered. All aspects of design, theory and realization are of interest. The scope and interests for the special issue include but are not limited to the following list: (i) Security & Privacy in AI ● Security AI modeling and architecture ● Secure multi-party computation techniques for AI ●Secure experiments, test-beds and prototyping systems for AI ● Novel cryptographic mechanism for AI ● Accelerated Machine Learning (ML) in a security environment ● Adversarial example (AE) research ● Generate Adversarial Network (GAN) research ● Attack and defense methods with AE ●Privacy-preserving ML ●Normative approaches to privacy in AI ●Security & privacy in robust statistics ●Security & privacy in online learning ●Adaptive side-channel attacks ●Security protocols for AI ●Security and privacy in data mining and analytics (ii) AI-Enabled Secure Emerging Applications ● AI for IoT security ● Privacy persevering ML in social network ●AI for spam detection ●AI for phishing detection and prevention ●AI for botnet detection ●AI for intrusion detection and response ●AI for malware identification ●AI for authorship identification ●AI for multimedia data security ● AI for enhance Privacy-Enhancing Technologies (PETs) ● AI-driven personalization of privacy assistance ● Vulnerability testing through intelligent probing ● AI -driven simplification or summarization of privacy policies ● AI analysis of privacy regulations ●AI systems defending against multiple attack vectors ●Biometrics security
Last updated by Dou Sun in 2020-03-18
Special Issue on Artificial Intelligence for Cyber Defence and Smart Policing (AICDSP)
Submission Date: 2021-01-10

Personal computers, laptops and personal smart devices have had a steady increase in storage and computational capacity capabilities over the years, where it has become common with terabytes in storage space. Moreover, the emergence of the Internet of Things and Smart Applications bringing a new horizon into how the data affects our life and the world around us. Despite the role of modern technologies in improved quality of life and making the world better place, the surface of cyber threats and anticipated cyber-attacks has been brought to a new level, as it is seen by ransomware and Mirai IoT botnets. Conventional Computer Forensics is no longer efficient because the term Computer has acquired much broader meaning over the last decades. Multiple aforementioned disruptive technologies result in the agile ICT environment, which constantly changes its state as a response to external influence. Moreover, previously unseen things like IoT orphan devices become a standard practice due to the relative inexpensiveness of the technologies, connectivity and power supply. We are surrounded by interconnected components, which we might not know about, and those collect personal data, sensitive information and, often, has multiple security vulnerabilities. Big data paradigm is undeniable in every aspect of cybercrime investigations. As a result, there is a strong need for novel methods in future to aid cybercrime investigations and police on large-scale data. Therefore, it is essential to look for an advanced Artificial Intelligence method capable of handling such challenges and bringing down the amount of manual labour required by police officers. Finally, providing more efficient data handling and digital evidence discovery will build a strong foundation for intelligent decision support in future smart cities across the globe.
Last updated by Dou Sun in 2020-05-27
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