Journal Information
Future Generation Computer Systems (FGCS)
Impact Factor:

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 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 Senti-Mental Health: Future Generation Sentiment Analysis Systems
Submission Date: 2020-12-01

The integration of several disciplines and their integration and deployment into the living environment are the ingredients for the design and development of future generation solutions. In this special issue, we intend to strengthen the link between the sentiment analysis field and the mental health research area. Within the Digital Health domain, several works demonstrated how the real-time monitoring of mood conditions led to an improvement of the overall patients and citizens quality of life. As an example, we want to mention the impact that emotion monitoring has on the improvement of daily healthy behavior (e.g. diet and physical activity) or how it works as a driver for reducing the exacerbation of undesired events for patients suffering from some chronic conditions. One-on-one interviews have always constituted a common technique to derive meaningful insights to draw comprehensive analysis. This occurs in several domains. For example, in the business scenario, customer discovery is targeted by means of one-on-one interviews to obtain insights about product features, pricing and launching strategies. Also, within the health domain, clinical interview transcripts have been used to distinguish different patient behavior types and mental statuses in order to design effective interventions for many conditions and disorders. Or again, a client-centered counseling style for eliciting behavior change by helping clients to explore and resolve ambivalence. Therapists attempt to influence clients to consider making changes, rather than engaging in non-directive therapeutic exploration. Interviews help in providing qualitative analyses although they are subjective and are affected by the unconscious biases of the authors or the researchers. This may increase the burden of researchers especially when transcripts show a general trend derived from linear models. Approaches and techniques to identify the objectivity to the interpretation of personal interviews to derive significant insights are therefore needed. To note that an expanding collection of video clips have been released to aid in a deeper understanding of motivational interviewing, diversity and concepts of change. Starting from these, multi-modal future generation sentiment analysis systems should be devised to give the therapists all the possible information and emotional sphere of the patient and thus provide better counseling. In this special issue we promote the submission of contributions integrating the sentiment analysis and mental health domains for empowering patients and domain experts in performing effective and efficient real-time monitoring of patients’ conditions. Finally, given the sensitivity of the data type treated within these systems, contributions on managing the privacy of emotional data are welcome. The proposed special issue fosters interdisciplinary research for communities working on Artificial Intelligence, Semantic Web, Natural Language Processing, Image and Signal Processing, Big Data, Sensor Networks, Psychiatry and more joining their forces in order to develop Future Generation Sentiment Analysis Systems. Last but not least, the special issue is sponsored by the PhilHumans and ValueCare projects. PhilHumans,, is a Marie Curie European Industrial Doctorate. Its aim is to train a next generation of young researchers in innovative Artificial Intelligence and establish users’ interaction with their personal health devices in an advanced and intuitive way by exploring cutting-edge research topics related to AI-supported human-machine interfaces for personal health services. While ValueCare,, aims to deliver efficient outcome-based integrated (health and social) care to older people facing cognitive impairment, frailty and multiple chronic health conditions in order to improve their quality of life (and of their families) as well as the sustainability of the health and social care systems in Europe. Topics of Interest Multi-modal Sentiment Analysis for motivational interviews Natural Language Processing systems for interview transcripts Image processing for clinical interviews Video analysis for clinical interviews Visualization or structural analysis of clinical interviews Clustering algorithms for clinical interviews Descriptive and linguistic analysis for clinical interviews Opinion search for clinical interviews Mental health Emotion detection and management Emotional conversational agents Explaining emotions Multi-modal emotion detection Sensor-based emotion detection Knowledge-based emotion analysis
Last updated by Dou Sun in 2020-08-10
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
Special Issue on Advances in Intelligent Systems for Online Education
Submission Date: 2021-01-15

The education sector is relying more and more on online learning. Educational and training institutions are being motivated to endorse online learning strategies thanks to its technical, economic, and operational feasibility. This has become even more important after the lockdown caused by the breakout of COVID-19, when even universities revised their educational strategy, moving to online teaching. Learners and teachers are benefiting from the flexibility, accessibility, and costs of learning and teaching online. Nonetheless, moving education online is bringing unprecedented challenges. For instance, learners may feel isolation online, massive content alternatives often overload learners and teachers who look for educational resources, and institutions are being challenged to ensure academic integrity in online exams. The increasing amount of learning-related data and high performance computing are enabling intelligent systems that can support stakeholders while facing current challenges. Bringing this intelligence to online education leads to a very wide range of advantages, e.g., avoiding manual error-prone tasks or providing learners with personalized guidance. As artificial intelligence research and development is getting more mature and the corresponding outputs are being deployed at scale in real-world contexts, the crucial role of using automated systems in online education, as an additional support for stakeholders during decision making processes, becomes more evident nowadays. Current research has greatly expanded our understanding on such artificial intelligence, but there has been less work on how it applies to online education. Data, methods, tools, and applications in this area are still limited, though they promise to proliferate in next years. Further, more research and many questions remain to be answered to bridge technological, social, pedagogical, and ethical perspectives in these intelligent systems. This Special Issue aims to present high-quality, high-impact, original research results reporting the current state of the art of online education systems empowered with artificial intelligence (e.g., machine/deep learning). We are interested in submissions covering different levels of the experimental pipeline, including but not limited to data collection, computational models, and applicative systems. We also invite prospective authors to share experience with dealing with online education in these months of COVID-19 emergency, technological changes that happened at the institution, and impact of the devised intelligent systems in their ecosystems. We are interested in contributions targeting automated intelligent support in online education applications, focused but not limited to the following areas. We seek to receive papers that clearly state and contextualize how the proposed intelligent system or tool is integrated in the real-world scenario and concretely supports stakeholders during decision-making. If in doubt about suitability, please contact the Guest Editors. ● Data Set Collection ○ New tools and systems for capturing educational data (e.g., eye-tracking, motion, physiological, etc.). ○ Proposals of procedures and tools to store, share and preserve learning and teaching traces. ○ Annotation standards and schemas for data that can be leveraged for machine learning. ○ Collecting and sharing data sets useful for applying machine learning in online education contexts. ● Model, Tool, and System Design ○ Semantic-based retrieval of instructional materials to identify appropriate contents. ○ Tools for adaptive question-answering and dialogue or automatically generating test questions. ○ Personalized support tools and systems for communities of learners (e.g., recommendation). ○ Content analysis for exam scoring and/or assessment. ○ Behavioral and physiological analysis of learners while interacting in online education platforms. ○ Student engagement assessment via machine-learning techniques (e.g., sentiment analysis). ○ Systems that detect and/or adapt the platform to sentiment or emotional states of learners. ○ Techniques to provide automated proctoring support during online examinations. ○ Tools able to predict the dropout risk of learners along the educational path. ● Evaluation Protocol Design and Conduction ○ Evaluation techniques relying on computational analyses in online education contexts. ○ Interpretability and/or fairness of the models and the resulting impact on real-world adoption. ○ Error analysis devoted to understanding, measuring, and managing uncertainty in model design. ○ Strategies to evaluate effectiveness and impact of intelligent systems on educational environments. ○ Exploration of cognition, affect, motivation, and attitudes of stakeholders, while deploying systems. ● Ethics and Privacy Investigation ○ Analysis of issues and approaches to the lawful and ethical use of intelligent systems. ○ Tackling unintended bias and value judgements in intelligent systems. ○ Regulations and policies in data management ensuring privacy while designing intelligent systems. ○ Broad discussion on potential and pitfalls of intelligent systems for educational contexts. ○ Studies on how teachers can be made part of the loop as moderators instead of being replaced.
Last updated by Dou Sun in 2020-08-24
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