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This page gathers for each of the call topic supported by CHIST-ERA:

  • A link to the Call Announcement;
  • The joint topic presentations presented at the Projects Seminar (*);
  • And a final analysis of the call topic (**).

(*) At the yearly Projects Seminar -an event gathering all ongoing projects- the projects of a given topic compiles a joint presentation to summarise their main results and draw the future and main challenges of this topic. The topic projects produce a first joint presentation at their start, which is then updated every year during the lifetime of the projects.

(**) At the end of the call topic lifecycle, when all funded projects are finished, CHIST-ERA produces a summary of the main outcomes, future challenges and related European Commission calls.

Open & Re-usable Research Data & Software (ORD)

This call tackles the challenge of open research data and software from the perspective of their  possible reuse. The objective is to create the conditions for research in any domain1 based on open  or shared data and software.
The call aims at contributing to develop and disseminate appropriate research practices across the various research communities and countries. For this purpose, the researchers and their scientific communities are invited to propose, by given discipline, emerging discipline or in an interdisciplinary approach how to apply the principles of Open Science to promote reuse of research data and software. The aim is to accelerate the emergence of related tools, standards and services.
The call covers all types and versions of data and associated metadata: Collected, captured, acquired, transformed, simulated, synthetic etc. ‘Research software’ stands for software produced by researchers, and used as enablers for research activities.

Security and Privacy in Decentralised and Distributed Systems (SPiDDS)

Ease of access to numerous computational resources, communication channels and increasing data volumes has led to increased interest in decentralised and distributed systems. Decentralised and distributed architectures provide advantages such as the ease of scalability, increased fault tolerance and faster data access. New devices can be more readily configured and added to the network with minimal interference in a decentralised or distributed network, whilst these systems are more resilient with no ‘central point of failure’. Whilst there are advantages from the network not relying on a single node, this can lead to a greater number of attack vectors. Depending on how the system is implemented, security can be weaker in both decentralised and distributed systems. Additionally, these systems are more complex to maintain, and whilst privacy can be enhanced, this also provides more scope for cyberattacks. As decentralised and distributed systems become increasingly used, solutions to ensuring privacy and security in a trade-off with performance are sought.

Machine Learning-based Communication Systems, towards Wireless AI (WAI)

In recent years, we have seen the rapid growth of mobile communications and Internet of Things (IoT) networks. This trend is expected to continue, with global traffic set to increase multi-fold over the next 5 years. This poses challenges for traditional networks with respect to their design, deployment, operation and optimisation. Future service requirements will include transfer of higher data volumes with ultra-low latency, improved connectivity, increased reliability and reduced power consumption. 
The next wireless networks should be able to meet the complex scenarios and non-linearity of future environments. Artificial intelligence (AI) is therefore key to achieving future requirements and dynamicity. Wireless AI looks at the implementation of Machine Learning (ML) techniques in Wireless communication systems to improve decision making, network management, and resource allocation. 
Therefore, in this call we look to discover new solutions to these problems, and create new application scenarios.

Nano-Opto-Electro-Mechanical Systems for ICT (NOEMS)

The combination of optical, mechanical and electronic elements into a single platform allows the manipulation of light via mechanical manipulation and electronics. In reducing the size of these metastructures (to the nanometer range) the strength of the interactions are enhanced and power consumption reduced as the mechanical elements require less power than electro-optic and thermo- optic effects, for example. Specifically, it is the exploitation of the piezo-electric and flexible states of light (intensity, phase, polarization and orbital angular momentum) that brings about a mechanical response which reduces the power consumption. Integration of NOEMS with processing chips potentially opens up applicability of NOEMS to various areas such as information carrying, communication, sensing and computing.

Foundations for Misbehaviour Detection and Mitigation Strategies in Online Social Networks and Media (OSNEM)

The pervasive nature of internet use, in particular social networking and media has come with immense benefits but also some drawbacks to society. The sharing of culture, experiences and news has increased awareness and understanding across different societal groups. However, these same networks can be used to spread misinformation, low quality news and fake news. Misbehaviour from bots and/or anonymous users can impact political outcomes, social inequalities, and health. This is increasingly spread beyond the text format, and now includes all types of media; image, video and audio. The majority of detection methods and mitigation strategies are only applicable to text and cannot be applied to other media types. This challenge is also exacerbated by the multi-platform and multi-language nature of social networks or media.

Advanced Brain-Computer Interfaces for Novel Interactions (BCI)

The challenge is to improve Brain Computer Interfaces (BCIs) as they become an increasingly explored technology most notably in the healthcare sector. Challenges with BCIs include the quality of brain signal received by the interface, variability in test subjects, lack of co-creation with users and data capturing. In addressing these, the applications of BCIs will be able to move beyond human-computer interactions but also include human to human interactions and human to object interaction. There is also an opportunity for passive BCIs.

Towards Sustainable ICT (S-ICT)

The increasingly complex nature of ICT has gone hand in hand with an increase in its energy demands. Sustainability needs to be applied across to all areas of ICT and bring about an overall reduction the negative impact ICT has on the environment. Harnessing novel approaches to modelling, materials, manufacturing and power management are all necessary to bring this to fruition. E-waste is also a major consequence of fast-moving developments in ICT, moving towards recyclable and modular devices that can be reused and/or repaired will lessen the burden on nations that take in the discarded electronics. It is equally important that end users are aware of the negative environmental impacts associated with ICT use.

Explainable Machine Learning-based Artificial Intelligence (XAI)

Explanation of decisions made by AI systems is seen as important for the trust and social acceptance of AI. It is likely in the future that there will be a ‘right to an explanation’ for decisions that affect an individual. The objective of research on this topic is to make machine learning- based AI explainable.

Novel Computational Approaches for Environmental Sustainability (CES)

With the challenge of environmental changes being highlighted, it is important that scientists are able to understand and model the environment so they can understand and predict upcoming changes. As environmental models become more complex and more adaptable in real time, it is necessary to change the way we work with these models, to be more integrative, more reactive and reduce the amount of computational power being used. This will improve the computational models that we have and allow better predictions on the future of our planet.

Analog Computing for Artificial Intelligence (ACAI)

Analog computing has seen its progress outpaced by the huge investments in digital computing following Moore’s law. However, with the end of Moore’s law, there is room again for analog computer architectures. These can enable fast, energy-efficient computing for specific applications and thus become attractive again. Analog computing becomes appealing for running AI applications locally on personal devices, and more generally in an energy-efficient way.

Smart Distribution of Computing in Dynamic Networks (SDCDN)

IoT solutions are driving the development of novel computing platforms that cope with the limitations of sensor/actuation devices and mobile devices. As a result, new computing paradigms that support diverse applications’ needs have arisen including cloud, fog and edge computing. Increasingly hybrid approaches are being adopted to provide performance trade-offs between these distribution models. Processing will have to be delegated via novel intelligent coordination strategies over dynamic networks, including cloud, fog and edge elements. There is a need for ubiquitous, context-aware, robust solutions that dynamically orchestrate computing tasks among these models.

Object recognition and manipulation by robots: Data sharing and experiment reproducibility (ORMR)

Projects should aim to enable the development of robots, which are able to accurately recognise and appropriately manipulate objects in various environments. Projects should lead to quantitative results which can be reproduced by others. Projects should address real-world challenges, and record and annotate robotic perceptions in order to experiment with different approaches for these challenges. Enough data from various environments and contexts should be used to show the robustness of the experimented approaches.

Big data and process modelling for smart industry (BDSI)

Industry is becoming increasingly digitized. Production and operational processes generate growing amounts of heterogeneous data, from simple sensor data to complex 3D video streams. This opens the way for new intelligent, flexible, network-centric production and operational approaches where parts, products and machines are interconnected across equipment, companies and value chains. The goal of these approaches is to enable production and operation at higher yield, higher quality, lower costs, lower environmental footprint and increased flexibility.

Lifelong Learning for Intelligent Systems (LLIS)

Intelligent systems are becoming pervasive in our daily life. However, they still lack the capability to learn from users or their environment and improve themselves without intervention from their initial developers. Such a capability of incremental autonomous learning, or lifelong learning, is a key to the development of truly autonomous intelligent systems. Projects aim at developing systems which offer state-of-the-art performance on a known task and are able to autonomously learn from further inputs in order to further improve on the same task or on an extended one.

Visual Analytics for Decision-Making under Uncertainty (VADMU)

Projects aim at enabling the creation of tools, demonstrable methodologies and assistive technologies for visual data analytics, in order to help organizations and institutions to rapidly recognize disruptive propositions and opportunities. Such tools and methods should be designed with full consideration of the user and application.

User-Centric Security, Privacy and Trust in the Internet of Things (SPTIoT)

The Internet of Things (IoT) vision, which benefits from the steady advances in microelectronics, communications and information technology, seems within reach. However, technical flaws in data security, both real and perceived, threats of intrusions and lack of transparency might significantly lower the uptake and benefits of the new technologies. For users to be confident, one must consider how they will be supported to understand how their data is collected, used, processed, accessed and kept safe. By providing users with this information, we empower them to understand and make their own decisions regarding their data.

Terahertz Band for Next-Generation Mobile Communication Systems (TMCS)

Within a decade a 1000-fold increase in wireless communication traffic volume is expected, requiring increased throughput and data rates. The Terahertz (THz) band, which remains largely unexplored, offers new possibilities. Spectrum frequencies over 275 GHZ are as yet unallocated and those over 200 GHz still present important opportunities to fulfil the demand for wireless data transmission. Exploring the spectrum at these frequencies offers new possibilities for employing large bandwidths for high-speed wireless communications. Further research on Terahertz band devices, models, networking techniques and (potential) applications are required.

Resilient Trustworthy Cyber-Physical Systems (RTCPS)

Cyber-physical systems (CPS) refer to novel hardware and software compositions creating smart, autonomously acting devices, enabling efficient end-to-end workflows and new forms of user-machine interaction. CPS carry a high potential for creating new markets and solutions to societal hazards, but impose highest requirements to quality in terms of resilience, safety, security and privacy. Foundational research efforts are needed to achieve a predictable quality level in an efficient, traceable and measurable way, coping efficiently with external and internal changes, supporting necessary transitions between mechanical, electrical and software engineering, as well as integrating management, design and deployment aspects.

Human Language Understanding: Grounding Language Learning (HLU)

Having a machine understand language like a human being is a long-held goal of AI. However, it is still far from reaching human performance. A fundamental difficulty is how to model high-level, semantic and pragmatic knowledge in a robust fashion. To overcome this difficulty, the machine learning approach which has proved very successful to train linguistic models from linguistic data should be extended to learn more general knowledge models from much more varied, multimodal data, in a more interactive setting, possibly granting important roles to the situational context of the words and to the internal state of the system. The goal is to ground language learning in the perceptual, emotional and sensorimotor experience of the system.

Adaptive Machines in Complex Environments (AMCE)

Autonomous systems are set to play an ever-increasing role in society, for example, in service robotics, assistive technologies, advanced manufacturing and many other sectors. To perform effectively and safely, these autonomous systems must be adaptive and perceptive to human requirements. Research in this topic addresses this challenge by developing autonomous systems that are perceptive to human requirements and that have the ability to continuously learn, adapt and improve in “real world” complex environments. These systems should be capable of continuous learning, such that they are able to work alongside humans in a reliable, safe and trust-worthy manner.

Heterogeneous Distributed Computing (HDC)

Heterogeneous distributed systems have the potential to increase computational performance while reducing energy consumption. The increase in the number of devices per capita and the challenge of processing ever-increasing amounts of data require new approaches involving researchers working across system levels. For example, hardware and software researchers working together to develop new approaches leading to improved performance, optimisation, reliability, fault tolerance and energy efficiency of distributed systems.

Intelligent User Interfaces (IUI)

Intelligent interfaces will expand from the screen, keyboard and mouse and into our life. Ultimately, all aspects of our life (body, emotions, history, etc.) can become the media of interaction. A new kind of user interfaces, whereby the user can interact with the computer in varied ways (multi-modal interaction), is needed. This computer builds an interaction model by combining multiple forms of interaction. It functions in the noisy real-life world. It not only relies on explicit commands, but also uses contextual information, learns from past interactions and behaviours, recognises emotions, makes sense of user intentions, identify social schemes, induce new behaviours among users, recovers gracefully from errors and misunderstandings, etc. It requires new ways of thinking about meaning, the interpretation of signs and the transfer of knowledge between the users and the system. The resulting interfaces are evaluated to be usable, natural, effective and empowering for users.

Context- and Content-Adaptive Communication Networks (C3N)

Projects are expected to deliver one or more of the following: Content and service-adaptive/centric; Network aware; Context-aware; Self configuration of applications; New approaches to network layering that better support any of the above.

From Data to New Knowledge (D2K)

The challenge is to produce new computational concepts, models, tools and methodologies to automatically and reliably extract new knowledge from large amounts of heterogeneous, unstructured data. Typical data include multilingual and multimedia data such as found on the web (text, speech, image, video...) and data generated by human organisations in the course of scientific, industrial or service activities (medical data, 3D object representations, advanced manufacturing data...). The goal of the call topic is to foster long term, highly innovative research and target new applications.

Green ICT, towards Zero Power ICT (G-ICT)

Project proposals address the issue of energy consumption in computation, information, sensing or communication systems from a global system perspective. Highly innovative approaches are expected at any of the system layers, from the nano-scale level to the architectural, software or protocol layers.

Beyond Autonomic Systems - the Challenge of Consciousness (BASCC)

There are no generally accepted scientific definitions of consciousness. For the purpose of this call, we focus on specific attributes, which CHIST-ERA considers pertinent. The creation of such systems is a major, long-term challenging goal, which will require substantial research and collaboration across many disciplines, including Computer Science, Automatics, Networks, Neurosciences, and others. The projects of this call topic seek excellent, innovative and multidisciplinary objectives which initiate research into systems that will exhibit at least two, and preferably more of the attributes of "consciousness" and propose new methodologies that will lay the foundation for the eventual achievement of these ambitious goals.

Quantum Information Foundations and Technologies (QIFT)

Projects address one or more of the following topics: Quantum information theory, algorit hms and paradigms; Scalability of quantum processing systems; Long distance quantum communication; Entanglement-enabled quantum technologies exploiting several qubits for performing ICT tasks, quantum simulators, quantum state engineering.