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

Special Issue on Machine Learning and Knowledge Graphs
Submission Date: 2019-11-30

Machine learning and knowledge graphs are currently essential technologies for designing and building large scale distributed intelligent systems. Machine learning is a well established field, which has currently gained a high momentum due to the advances in the computational infrastructures, availability of Big Data, and appearance of new techniques such as deep learning. In fact, Deep Learning methods have become an important area of research, achieving some important breakthrough in various research fields, especially Natural Language Processing (NLP), Image and Speech Recognition.

Knowledge Graphs are large networks of real-world entities described in terms of their semantic types and their relationships to each other. Knowledge graphs is a recent technology with very high practical impact: examples include the Google Knowledge Graph with over 70 billion facts (in 2016), dataCommons, DBPedia, YAGO and YAGO2, Wikidata and Knowledge Vault, a very large scale probabilistic knowledge graph created with information extraction methods for unstructured or semi-structured information. Specifically, Knowledge Graphs provide the means of development of the newest methods for data management, data fusion / data merging, and graph and network optimization and modeling, serving as a source of high quality data and a base for ubiquitous information integration. While Knowledge Graphs provide explicit knowledge representations in terms of underlying ontologies based on symbolic logic, machine learning, such as Deep Learning technologies, provide implicit or latent representations of the knowledge contained in their models.

Although machine learning (and particularly, recently deep learning) and Knowledge Graphs technologies have been deployed separately, in the last years, the first works combining these technologies are showing large potential in solving many real-world challenges.

In order to pursue more advanced methodologies, it has become critical that the communities related to Machine Learning, Deep Learning and Knowledge Graphs join their forces in order to develop more effective algorithms and applications. In particular, two main technology directions solicited for this special issue are as follows:

i) Improved Machine Learning with Knowledge Graphs: employing semantic models and linked data for the training steps, learning effective representation from the Knowledge Graphs for the tasks of feature extraction, classification, prediction and decision making. A successful example here includes IBM Watson question answering system, that has outperformed the best human players of an intellectual TV quiz show "jeopardy!"

ii) Machine Learning for Improving Knowledge Graphs: while capturing semantics correctly is impossible without at least some human involvement, machine learning can assist the knowledge acquisition of the semantic structures substantially. For example, knowledge graphs can be created by employing Deep learning, and then subsequently verified by the humans.

A feature that explains the learnt knowledge graphs to the human needs to be inbuilt in the learning and verification process, making the resulting knowledge graph design solutions explainable to humans. Thus, the knowledge graphs created in this manner may be more efficient and provide more insights than the ones created solely by humans.

The solicited contributions may comprise an overview of the outlined fields, and new technical solutions applicable to such use cases as manufacturing / smart factory, production 4.0 and its control of quality, mobility, smart cities, smart homes and buildings, energy efficiency, health and wellbeing, life sciences, libraries and archives, art.

Such use cases require both the analysis of data (e.g. sensor readings, distributed data processing) - i.e. quantitative information, and as well as qualitative (e.g. information semantically defining the production quality criteria and what is considered to be the deviation from them). As physical infrastructures and architectures (stemming from the fields of Grid and Cloud Computing, Big Data and Internet of Things), have a very high impact on the solutions to be suggested, we consider that Future Generation Computer Systems is the perfect venue for publishing this special issue, given the research area of the journal.

Topics of Interests:

We invite submission of high-quality manuscripts reporting relevant research in the area of generation of knowledge graphs by using deep learning techniques. Topics of interest include, but are not limited to:
Architectures for systems based on Machine Learning and Knowledge Graphs
Machine Learning and Knowledge Graphs in distributed large scale systems
Information management with on Machine Learning and Knowledge Graphs
Probabilistic Knowledge Graphs
Creation and maintenance (curation, quality assurance...) of Knowledge Graphs employing Machine Learning
Machine Learning techniques employing Knowledge Graphs
Explainable Artificial Intelligence for data-intensive systems based on Knowledge Graphs
Non-functional application of Knowledge Graphs in data-intensive systems e.g. for legal use of data, user consent solicitation, smart contracting, GDPR
New approaches for combining Deep Learning and Knowledge Graphs
Methods for generating Knowledge Graph (node) embeddings
Scalability issues
Temporal Knowledge Graph Embeddings
Applications of combining Deep Learning and Knowledge Graphs
Recommender Systems leveraging Knowledge Graphs
Link Prediction and completing KGs
Ontology Learning and Matching exploiting Knowledge Graph-Based Embeddings
Knowledge Graph-Based Sentiment Analysis
Natural Language Understanding/Machine Reading
Question Answering exploiting Knowledge Graphs and Deep Learning
Entity Linking
Trend Prediction based on Knowledge Graphs Embeddings
Domain Specific Knowledge Graphs (e.g. Smart Cities, Scholarly, Biomedical, Musical)
Applying knowledge graph embeddings to real world scenarios.

Special Issue on Future Generation of Service-Oriented Computing Systems
Submission Date: 2019-12-15

Services computing has been a subject of intensive research, development, and deployment in the last decade. Since more and more private users, research institution, businesses, hospitals, cities, and industry companies wish to benefit from interconnectivity, provided in particular by wireless networks, the interest in services study and development is growing exponentially. The areas of use of services computing are heterogeneous, subject to security attacks, and high-performance, reliability, and trustworthiness expectations of users. From the first concepts of services linked to the abstractions of infrastructure, platforms, and software, we face the services created for and exploited in data storage and management, fogs, edges, security, trust, workflow, modelling, etc. This imply a need for new knowledge of services cloud, enhancing existing knowledge of in the area of services, and generate new applications that would benefit other researchers, developers of service computing systems, and create a basis for other users to consider making their areas of interest better, faster, more secure, and trustworthy.

The aim of this Special Issue is toreport the state of research on Future Generation of Service- Oriented Computing Systems. It will be comprised of selected papers drawn from submissions from extended peer-reviewed versions of the best papers presented at the 17th International Conference on Service-Oriented Computing (ICSOC), will take place in Toulouse, France, in October 28–31, 2019 (http://www.icsoc.org). ICSOC is the premier international forum for academics, industry researchers, developers, and practitioners to report and share ground breaking work in service-oriented computing. ICSOC fosters cross-community scientific excellence by gathering experts from various disciplines, such as business-process management, distributed systems, computer networks, wireless and mobile computing, cloud computing, cyber-physical systems, networking, scientific workflows, services science, data science, management science, and software engineering.

The SI seeks outstanding, original contributions, including theoretical and empirical evaluations, as well as practical and industrial experiences, with emphasis on advanced results that solve open research problems and have significant impact on the field of service-oriented computing. Specific topics of interest include but are not limited to:

1.Service Engineering and Management
o Legacy systems migration and modernization
o Service design, specification, discovery, customization, composition, and deployment
o Service innovation, governance, and change and workload management
o Theoretical foundations of Service Engineering
o Service execution, monitoring and reconfiguration
o Quality of service, Security, privacy, and trust
o Architectures for multi-host container deployments
o Microservices deployment and management

2.Services and Data
o Services for Big Data and compute-intensive applications
o Mining and analytics
o Data-provisioning services
o Services-related linked open data
o Automated Knowledge Graph creation

3.Services in the Cloud and on the Edge
o Migration to virtual infrastructures
o XaaS (everything as a service including IaaS, PaaS, and SaaS)
o Service deployment and orchestration in the Cloud
o Cloud and Edge service and workflow management
o Cloud and Edge brokers and coordination across multiple resource managers
o Workload transformation
o Analytics and knowledge generation services
o Lightweight service deployment and management
o Services and edge gateway architectures

4.Services for the Internet of Things
o Embedded and real-time services
o RFID, sensor data, and services related to the Internet of Things
o Services for IoT platforms and applications
o Service-oriented protocols for IoT applications
o REST APIs and services for IoT platforms and applications

5.Services for Softwarized Network Functions and Software Defined Networks
o Service Network Function Management and Orchestration
o Services for novel and emerging networking protocols
o Named data networking
o Virtualized Network and transport mechanisms
o Virtualized Network Functions and Services
o Virtualized Service Function Chaining

6.Services in Organizations, Business, and Society
o Services science
o Social networks and services
o Cost and pricing of services
o Service marketplaces and ecosystems
o Service business models
o Enterprise architecture and services
o Service Chatbots

Special Issue on Advanced Techniques and Emerging Trends in Smart Cyber-Physical Systems
Submission Date: 2019-12-15

In the vision of Industry 4.0, the new industrial revolution is a revolution of cyber-physical systems, of which the Internet of Things forms a key foundation that has a great impact on the way people live and the way businesses are organized. Cyber-physical systems are often considered feedback systems that integrate computation, networking, and physical processes. The CPSs control the physical processes by utilizing the artificial intelligence to acquire the deep knowledge of the monitored environment. Hence the CPSs are designed to be intelligent to provide highly accurate decisions and appropriate actions promptly. The rapidly growing interconnections between the virtual and physical worlds and the development of new intelligent techniques have created new opportunities for the research for next-generation CPS, that is smart cyber-physical systems (SCPS). The SCPS are large-scale software intensive and pervasive systems, which by combining various data sources (both from physical objects and virtual components) and applying intelligence techniques, can efficiently manage real-world processes and offers a broad range of novel applications and services. 

Components of a SCPS must have a high degree of autonomy while cooperating with each other in a robust, scalable and decentralized way. However, several challenges need to be overcome in order to realize such a paradigm, which is highly multidisciplinary. These challenges range from the design of intelligent physical infrastructures for sensing and communication, data stream processing, data analytics and machine learning techniques to build the intelligence core of these systems through the development of self-adaptive and context-aware software. Moreover, safety, social and behavioral issues also need to be considering, when including human beings as an integral part of these highly complex systems. As CPSs hold strong interactions between the cyber and physical components, it plays a significant role in the development of next-generation efficient-smart systems in various real-time applications. 

This special issue is intended to report high-quality research on recent advances towards the realization of the Smart Cyber-Physical Systems paradigm. We are interested in all aspects pertaining to this multidisciplinary paradigm, in particular, in its application to building Smart and sustainable spaces. This special Issue will report the recent increasing interests in the design and development of intelligent techniques for various applications of Smart Cyber-Physical Systems. Moreover, the authors are expected to investigate state-of-art research issues, architectures, applications and achievements in the field of Cyber-Physical Systems. Unpublished innovative papers which are not currently under review to another journal or conference are solicited in the following relevant areas. 

Topics of interest include, but are not limited to: 

Data management and knowledge representation for Cyber-Physical Systems 
Algorithms, models, and designs for social Cyber-Physical Systems 
Machine learning for high-performance computing with Cyber-Physical Systems 
User activities recognition for Cyber-Physical Systems 
Cryptographic protocols and algorithms for Smart CPS 
Cryptographic engineering for CPS or IoT devices 
Data integrity, authentication, and access control for Smart CPS 
Security in smart grids/smart homes/smart cities/smart transportation 
Security threat detection theories and technologies 
Cloud based secure Smart CPS 
Privacy issues in smart grids/smart homes/smart cities/smart transportations 
Human Computer Interaction in SCPS 
Analysis of Network Dynamics in Cyberspace 
Knowledge Modeling and Management in Cyber-Social Networks 
Cyber-Social Data Processing and Intelligence Mining 
Cyber-Physical Hybrid System Design 
Trust and Reputation in Social Cyberspace 
Cyber-Enabled Learning Analytics 
Cyber-Physical Healthcare Services 
Cyber-Empowered Sentiment Analysis and Mental Computing

Special Issue on Self-integrating Systems: Mastering Continuous Change
Submission Date: 2019-12-20

The goal of this special issue if to highlight the current state-of-the-art in self-improving system integration and to foster novel developments. This corresponds to two closely coupled concepts: self-integration and self-improvement.

Self-integration: In engineering, the notion of integration describes a process in which several component (sub-)systems are brought together and interconnected into a unified system. This aims to achieve a correctly working unit, where the subsystems work together to provide desired functions, with acceptable performance and dependability properties. In classic engineering, the integration of (sub-)systems has been done at design-time, with rigid specifications and testing of (sub-)system interfaces and performance. However, with increasingly networked and open systems (e.g., internet of things, smart homes, cities and electric grids), we now face the challenge of integrating systems dynamically, as rapidly as possible. Because of the dynamic contexts - where goals, resources, and knowledge required for integration change rapidly - increasing efforts have been directed towards new processes and computations that allow intelligent systems to do most of the integration themselves. Hence, self-integration is defined as an ongoing autonomous process for linking a potentially large set of heterogeneous computing systems, devices, and software applications; so as to meet system goals. The linking itself is done physically or functionally. We consider this process to be continuous, i.e. it is never finished, as the way in which the integrated elements - software and hardware - act together must adapt to external changes, goal evolutions and autonomous decisions of these elements.

Within this process, two major tasks are performed: (i) connections between subsystems are established, tested, and assessed; and, (ii) the particular subsystems in combination with their behavioural strategies are configured depending on the operational and functional area. This has a strong overlap with the concept of "self-organisation": (i) connecting subsystems or elements refers to building the system's structure; and, (ii) configuration is mainly concerned with defining the desired behaviour. It also relies on concepts from self-aware computing systems that use internal knowledge and learning processes to reason about their resources and state. Multi-agent systems also provide relevant concepts in terms of organisational paradigms, where agents can cooperate or compete, forming hierarchies, coalitions, teams, federations, societies and so on.

Self-improvement: implies that self-integrating systems do not settle for the status quo, but rather rely on continuous learning to optimise their behaviours with respect to their goals; even though they may or may not be able to reach optimal states or behaviours.

In this special issue, we aim to summarise the current developments in autonomous and self-improving system-integration. We expect contributions to cover at least one of the following aspects of self-integration (although this list is by no means exhaustive, leaving the call open to other related contributions):

- Goal specifications and conflict management for self-integrating systems
- Performance and dependability aspects and quantification methods
- Learning techniques for self-improving self-integration
- Learning techniques that work with sparse feedback and/or prior knowledge
- Modelling expected behaviour and mutual influences in system-of-systems constellations
- Real-time prioritization; representation of relevancy and importance
- Runtime model integration (i.e., Models@runtime) also takling timing into consideration (i.e., real-time restrictions)
- Decision and planning techniques for self-adapting the integration status at runtime
- Mechanisms for guiding the behaviour of autonomous entities without direct intervention (e.g. using norms)
- Runtime system verification and validation
- Security issues and guarantees in self-integrating systems
- Testbeds and benchmarks for self-integrating systems

Special Issue on Internet-of-Things and Cyber-Physical System in Smart City
Submission Date: 2019-12-30

With the rapid development of big data and current popular information technology, the problems include how to efficiently use systems to generate all the different kinds of new network intelligence and how to dynamically collect urban information. In this context, Internet-of-Things and powerful computers can simulate urban operations while operating with reasonable safety regulations. However, achieving sustainable development for a new urban generation currently requires major breakthroughs to solve a series of practical problems facing cities.

A smart city involves wide use of information technology for multidimensional aggregation. The development of smart cities is a new concept. Using Internet-of-Things technology on the Internet, networking, and other advanced technology, all types of cities will use intelligent sensor placement to create object-linked information integration. Then, using intelligent analysis to integrate the collected information along with the Internet and other networking, the system can provide analyses that meet the demand for intelligent communications and decision support. This concept represents the way smart cities will think. This type of behavior, with information technology as the background, can establish a practical, strong, and perfect application system for public management, public services, and public industry, improve governmental service efficiency, and improve people's quality of life. A smart city emphasizes efficient information processing abilities, integration of information resources, and management capabilities so that all parts become more coordinated. People, objects, networks, and industry become interconnected and mutually aware through interdisciplinary, cross-sectional, multi-level, and cross-regional cooperation, resulting in a realization of new models and new forms of urban development, all of which represent the wisdom of smart cities.

Cyber-Physical System(CPS) as a multidimensional and complex system is a comprehensive calculation, network and physical environment. Through the combination of computing technology, communication technology and control technology, the close integration of the information world and the physical world is realized. IOT is not only closely related to people's life and social development, but also has a wide application in military affairs, including aerospace, military reconnaissance, intelligence grid system, intelligent transportation, intelligent medical, environmental monitoring, industrial control, etc. Intelligent medical system as a typical application of IOT will be used as a node of medical equipment to provide real-time, safe and reliable medical services for people in wired or wireless way. In the intelligent transportation system, road, bridge, intersection, traffic signal and other key information will be monitored in real time. The vast amount of information is analyzed, released and calculated by the system, so that the road vehicles can share road information in real time. Personnel of road management can observe and monitor the real-time situation of the key sections in the system, and even release the information to guide the vehicle so as to improve the existing urban traffic conditions. The Internet of things, which has been widely used in the industry, is a simple application of IOT. It can realize the function of object identification, positioning and monitoring through the access to the network.

This special issue calls for high quality and up-to-date technology related to IOT and CPS and serves as a forum for researchers all over the world to discuss their works and recent advancements in the field. In particular, the special issue is going to showcase the most recent progress and development in IOT discovery and exploration. Both theoretical studies and state-of-the-art practical applications are welcome for submission. All the submitted papers will be peer-reviewed and selected on the basis of their quality and relevance to the theme of this special issue.

Topics of interest include, but are not limited to, the following scope:

Integrating provenance into Internet-of-Things(IoT) and Cyber-Physical Systems(CPS)
Internet of Things and Internet of Vehicles
Real-time meanings in various application spaces: aeronautics, medical, transportation, energy
Pervasive and Ubiquitous Technology in IoT
Spatial data mining in IoT
Multimedia Communications and Visual Signal Processing in IoT
Physical/virtual testbeds for real-time closed-loop control
Robot navigation in IoT
Knowledge reasoning in physical systems
Machine learning and other approaches for real time data analytics
Data-driven urban transportation management

Special Issue on Authentication, Identification and Identity Management Techniques in Blockchain
Submission Date: 2020-01-05

Users are often not in control over their personal identifiable information (PII) and digital information is easy to be collected, analyzed and cloned. Hence, PII is subject to be shared without awareness. Lots of massive data breach events show that users' PII are often stored in plaintext and privacy issues have been the primary concern of users. Furthermore, in General Data Protection Regulation (DGPR), industries are required to protect users' PII and privacy. Compliance and regulations are driving us to develop new identity management systems. Blockchain is a decentralized, distributed and public digital ledger and can be used to record transactions among multiple parities. Hence, blockchain can be potentially used to manage users' PII in a decentralized way. However, how to enable users to store their PII on blockchain and use it without privacy breach is challenging. Furthermore, how to realize authentication and identification in decentralized systems is interesting.

The aim of this special issue is to promote research and reflect on authentication, identification and identity management in blockchain, with emphasis on the following aspects, but certainly not limited to:

Accountability in blockchain
Anonymous credential
Authentication in blockchain
Anonymity in blockchain
Distributed authentication
Distributed identification
Identity management in smart contract
Legal and regulation issues in blockchain
Privacy-enhanced techniques
Privacy issues in blockchain
Pseudonym
Ring signature
Succinct non-interactive zero-knowledge proofs
Zero-knowledge proof

Special Issue on Intelligent IoT Systems for Healthcare and Rehabilitation
Submission Date: 2020-01-10

The growing trend of the aging population poses many challenges for the global society. Many of the challenges relate to the healthcare and rehabilitation needs of the elderly; these needs require significant time, resources, and manpower. The emerging field of flexible bio-mechatronic technologies, such as wearable sensors, actuators and robots, will play a crucial role in the design and development of next generation healthcare and rehabilitation systems that safely and smartly interact with human patients. Rapid advances in developing and implementing such wearable sensors, actuators, Internet of Things (IoT) and intelligent algorithms have demonstrated the growing significance, potential utility, and the unique advantages that can be bought to intelligent healthcare and rehabilitation systems. This special issue (SI) aims to bring together researchers and practitioners to discuss various research and development achievements in the field of multi-sensor based intelligent IoT systems for intelligent healthcare and rehabilitation applications. Potential contributions include work related to novel design and development of biological sensors, IoT infrastructures, soft robotics, medical prosthetics, biomechanical modelling and health monitoring, as well as exploring novel algorithms to analyze the data and address security and privacy issues in this field. Addressing these challenges has attracted increasing attentions from researchers in recent years.

This special issue will provide opportunities for researchers and practitioners to publish their latest innovative contributions in the areas of intelligent IoT systems, such as sensors, actuators, and data processing, in the context of rehabilitation and biomedical healthcare applications. These contributions could address state-of-the-art developments and methodologies, as well as applications of wearable devices and perspectives examining the future of IoT based healthcare systems. The special issue will attract readers from different research areas, including novel algorithms and applications for healthcare infrastructures, big health data analysis, as well as devices and tools for health monitoring and rehabilitation. Potential topics include but not limited to:

Wearable sensors, bio-sensor networks for IoT-based healthcare systems
Big healthcare and rehabilitation data analytics
Hardware and software related to wearable rehabilitation systems
Design, modeling, and manufacturing of flexible actuators for rehabilitation
Machine learning and artificial intelligence in intelligent IoT systems for rehabilitation
Rehabilitation and patient monitoring applications
High performance computing for intelligent rehabilitation
Data collection, analytics, security, and management
Scheduling algorithms for distributed systems

Special Issue on Advancing on Approximate Computing: Methodologies, Architectures and Algorithms
Submission Date: 2020-01-13

In the modern computing era, characterized by saturated performance and high production costs, Approximate Computing has been representing the most attractive breakthrough for efficient system design. Such an innovative paradigm leverages the intrinsic error resilience of applications to inaccuracy in their inner calculations, in order to trade output result quality, under a certain maximum acceptable error threshold, off for system performance gain, such as calculation time and power demanding. In particular, for audio, image and video processing, data mining and information retrieval, approximate results turn out hard to distinguish from perfect ones, while their computation is less expensive. In recent years, Approximate Computing applicability is broadening in many scientific areas since suitable solutions come from approximate arithmetic operators, implemented both at hardware and software level, but from unreliable memory architectures, integrated circuit test, compilers and many others too.

The special issue on "Advancing on Approximate Computing: Methodologies, Architectures and Algorithms" seeks for original contributions about the Approximate Computing paradigm; main areas of interest include, but are not limited to, the following:

Modeling, specification, and verification of approximate circuits and systems
Test and fault tolerance of approximate circuits and systems
Dependability of approximate circuits and systems
Error Resilient Near-Threshold Computing
Computing on unreliable hardware
Approximation induced error modeling and propagation
On-line test, monitoring and reconfiguration of approximate circuits and systems
Applications and case studies
Software-based fault tolerant technique for approximate computing

Special Issue on Internet of People: Human-driven Artificial Intelligence and Internet for Smarter Hyper-Connected Societies
Submission Date: 2020-01-20

The rise of wearable/embodied technologies and personal/body area networks has successfully bridged our personal physical and cyber worlds in recent years. Interactions between human users and their personal mobile devices push toward an Internet where the human user becomes more central than ever (evolving from consumers of information to prosumers), and where their personal devices become their proxies in the cyber world, in addition to acting as a fundamental tool to sense the physical world (what is also called as citizen science or participatory sensing). Internet of People (IoP) is a new Internet paradigm where humans and their personal devices are not seen merely as end users of applications, but become active elements of the Internet. It represents the mapping of social individuals and their interactions with smart devices to the Internet. It focuses on data collection, modelling, analysis and ubiquitous intelligence for a wide range of applications of crowd sourced, Internet-based personal information.

Likewise, the emerging social and sentiment analysis computing, behaviour modelling and novel human/environment interaction mechanisms, e.g. brain, audible or augmented or mixed reality computing, will allow further incorporation of the social and mental worlds into the so-called Hyper World. Due to the pervasiveness of IoP and its impacts on human activity, it has quickly emerged as an important interdisciplinary field which encompasses IoT, HCI, bio-inspired algorithms with behavioural and social sciences.

In essence, IoP promotes the idea that a Smart World is only possible if there is a better and smarter collaboration bridging machine intelligence and human intelligence, between devices and people, where technology (AI in particular) is applied from a human-centric and driven standpoint, i.e. following the Internet of People paradigm.

This special issue is then focused on cutting edge research from academia, industry and practitioners, with emphasis on original, novel, innovative and impact-oriented research providing insights into Smarter Hyper-Connected Societies' needs, processes, services and frameworks using the Internet of People paradigm. An area of particular interest in this Special Issue is to consider how Collective Intelligence can meet Machine Intelligence in order to give place to more user-centric and driven intelligence to enable Intelligent Environments where Humans and Internet-connected devices cooperate and influence their behaviour in order to promote autonomous life, better energy sustainability, healthier environments or inclusive environments.

Specifically, this issue welcomes three categories of papers: (1) high quality and significantly extended papers from the 13th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAml 2-5 December, 2019) whose motto is "user-centered computing environments and systems" and from the ACM 9th IoT Conference (IoT 22-25 October, 2019); (2) invited articles from qualified experts; and (3) contributed papers from this open call with address any of the following list of topics.

Topics of interest include use of Internet of People applied towards contributing towards a Smarter World and Society where Collaborative Human and Machine Intelligence are put into play, but are not limited to:

Social Computing and Social Internet of Things
Social Networks and Behavior Analysis
Behaviour Change Analysis
Internet of Sensing, Thinking and Creation
Gamification and user-engagement approaches
Human-Centric Computing and Cyber-Physical-Social Systems
Crowd Sensing and Human Intelligence
Hybrid machine and human intelligence
Biometric Sensors and Activity Recognition
Brain Information Sensing and Processing
Brain-Computer Interface/Brain-Machine Interface
Novel ubiquitous interaction mechanisms
Wearable Computing and Implant Technology
Personal Big Data Analytics
Edge/Fog based Models for Personal Computing
Sentiment Analysis and Affective Computing
AI-powered Smart Devices
Personal and Social Robotics
Personal Internet-based Healthcare, Wellbeing and Wellness
IoP Big Data Processing and Urban Computing
IoP Systems Modeling, Simulation and Optimization
IoP Security, Privacy, Trust, Psychology, Ethics, Politics and Laws

Special Issue on Emerging Topics in Defending Networked Systems
Submission Date: 2020-01-25

In recent years, novel security threats arose, be it due to sophisticated malware obfuscation, anti-forensics techniques, advanced methods of network steganography/information hiding, newer de-anonymization methods or improved social engineering approaches. Increasingly heterogenous and inter-networked environments allow such threats to become more difficult to combat, e.g., due to the ever-broader spectrum of IoT and CPS protocols and heterogenous hardware platforms, over-complex frameworks for inter-connectivity and professionalization and funding of attackers.

Researchers aim to address these new threats with the development of novel methods (countermeasures) for defending networked systems. This is challenging and important at the same time. One of the most important advancements proposed by the community of security experts (both from industry and academia) deals with new forms of traffic normalization or active wardens, which allow to mitigate attacks, but do not offer a comprehensive protection. Moreover, novel attacks target highly specific features of the system to be exploited, for instance, vulnerabilities of the hardware and its energy consumption and network side channels.

In this perspective, this special issue desires to foster the progress in research on the development of novel defense methods in information security, especially for sophisticated and networked/hyper-connected systems, including those within the IoT and CPS.

Topics of interest include (but are not limited to):

    Novel and effective countermeasures (techniques against modern threats, such as dynamic and adaptive countermeasures).
    Methods that increase the efficiency and effectiveness of countermeasures over the state-of-the-art.
    Surveys of defense methods in current domains of information security and surveys that systematize commonalities between different types of countermeasures.
    Evaluation of existing taxonomies and proposals for new taxonomies in cyber defense.
    Work that unifies terminological inconsistencies in cyber defense.
    Work that reproduces existing experiments, i.e., that confirms/disproves experimental results on the defense of networked systems, and that additionally proposes experimentally verified improvements.
    Work discussing methodologies to collect data and samples for modeling threats for the benefit of optimizing countermeasure design.
    Work that discusses the underlying criteria for the design and evaluation for cyber defense research testbeds.
    Work discussing machine-learning-based approaches for revealing unknown network-level threats.
    Methodology for privacy, information sharing and collaborative work in the context of cyber defense.
    "Open science" for cyber defense.
    Policy issues that influence cyber defense.

[Submission Instructions from the SI Information Form go here]

The FGCS' s submission system will be open for submissions to our Special Issue from Nov 15th, 2019. When submitting your manuscript please select the article type "VSI: Emerging Network Defense". Please submit your manuscript before Jan 25th, 2020.

All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.

Please ensure you read the Guide for Authors before writing your manuscript. The Guide for Authors and link to submit your manuscript is available on the Journal's homepage at: https://www.elsevier.com/journals/future-generation-computer-systems/0167-739x/guide-for-authors

Special Issue on Data Science for Smart Healthcare: Methodologies and Applications
Submission Date: 2020-01-30

Healthcare applications around the world are facing new challenges in responding to trends of aging population, the rise of chronic diseases, resources constraints, and the growing focus of citizens on healthy living and prevention.

Therefore, there is an increasing focus on how to improve the rate of fast and accurate diagnoses, how to reduce costs and outcomes in health systems and how to get people to take more accountability for their own health.

Novel ICT technology are enabling the collection of more data than ever before, including medical health records, people’s vital signs and their lifestyle and data about health systems.

This great amount of data does not immediately result in better healthcare insights, but, on the contrary, if not used properly, it can be a burden to people and result in clinicians spending more time with computers than face to face with patients, or citizens being lost in data they are getting from health trackers and many different sensors, or, again, patients reluctant to accept assistive technologies.

In this perspective Data Science and Machine Learning can provide benefits to both patient and medical professionals, also helping in answering the abovementioned questions related to the large amount of available data.

The objective of this special issue is to attract high-quality research and survey articles that promote research and reflect the most recent advances in addressing Data Science methodologies and applications for Healthcare.

We welcome researchers from both academia and industry to provide their state-of-the-art technologies and ideas covering all aspects of Data Science methodologies and applications for Healthcare.

Topics:

Potential topics include but are not limited to following:

• Artificial Intelligence models for Healthcare.
• Machine Learning models for Healthcare.
• Clustering and classification algorithms for Healthcare.
• Deep and reinforcement learning for Healthcare.
• Big Data analytics for data processing from Healthcare.
• Fuzzy Systems proposals for Healthcare.
• Expert/hybrid Systems for Healthcare
• AI/ML for IoT, Industry 4.0 for Healthcare
• Intelligent security proposals for Healthcare.
• Control systems developments for Healthcare
• Organization Based Multiagent Systems for Healthcare

Special Issue on Co-design of Data and Computation Management in Fog Computing
Submission Date: 2020-01-31

The term Fog Computing has been introduced to identify a paradigm for designing applications able to exploit both the (virtually) infinite resources on the cloud and the limited edge computation power by operating also on the devices living in between these two sides. More specifically, the aim is to exploit a heterogeneous and distributed computational and storage environment to optimize the execution of modern applications requiring high computational resources while reducing the delay and defining constraints on where and how data can be moved and stored. In fact, big volumes of data are produced everyday at the edge of the network and their analysis requires to either move the computation to the data or to move the data to the computation. For this reason, a co-design between data and computation management is required. An example could be to define the amount of data to be moved with respect to the complexity of required data analysis. In some cases, this data movement is not possible or limited due to privacy restrictions, which do not allow data collected at the edge to be stored on cloud facilities maintained by third-party actors. In other cases, the volume of data moved through the network introduces severe delays in the processing and techniques to reduce the volume by pre-processing and filtering them directly where the data are generated should be enacted to reduce this delay. At the same time, any transformation of the data might affect their utility for the final customer, thus, also the quality of the data should be taken into account. Moreover, the data collected by IoT and sensors at the edge is often subject to quality issues that might be detected and solved before using them for analytics and decision making.

This special issue aims to gather recent work on the topic of data and computation management in fog computing. Topics of particular interest include, but are not limited to:

● Management and control of Fog Computing resources and applications
● Data and computation management architectures in Fog Computing
● Data-driven offloading computation
● Security and privacy issues in data and computation management in Fog Computing
● Data movement, data duplication, and data distribution
● Energy efficiency in Fog Computing through data and computation movement
● Data quality assessment and improvement in Fog Computing
● Data processing in heterogeneous and/or distributed environments
● Internet of Things and Fog Computing
● Smart monitoring and anomaly detection for Fog resources and applications
● Simulation tools for Fog Computing
● Data processing platforms for Fog Computing
● Experiment-driven research on data and processing placement