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Importance of Data Science education and training is growing with the emergence of data driven technologies and organisational culture that intend to derive actionable value for improving research process or enterprise business using variety of enterprise data and widely available open and social media data. Modern data driven research and industry require new types of specialists that are capable to support all stages of the data lifecycle from data production and input to data processing and actionable results delivery, visualisation and reporting, which can be jointly defined as the Data Science professions family. The education and training of Data Scientists requires multi-disciplinary approach combining wide view of the Data Science and Analytics foundation with deep practical knowledge in domain specific areas. In modern conditions with the fast technology change and strong skills demand, the Data Science education and training should be customizable and delivered in multiple form, also providing sufficient data labs facilities for practical training. This paper discusses approach to building customizable Data Science curriculum for different types of learners based on using the ontology of the EDISON Data Science Framework (EDSF) developed in the EU funded Project EDISON and widely used by universities and professional training organisations.

Security is a top concern in digital infrastructure and there is a basic need to assess the level of security ensured for any given application. To accommodate this requirement, we propose a new risk assessment system. Our system identifies threats of an application workflow, computes the severity weights with the modified Microsoft STRIDE/DREAD model and estimates the final risk exposure after applying security countermeasures in the available digital infrastructures. This allows potential customers to rank these infrastructures in terms of security for their own specific use cases. We additionally present a method to validate the stability and resolution of our ranking system with respect to subjective choices of the DREAD model threat rating parameters. Our results show that our system is stable against unavoidable subjective choices of the DREAD model parameters for a specific use case, with a rank correlation higher than 0.93 and normalised mean square error lower than 0.05.

Current agent architectures implementing the belief-desire-intention (BDI) model consider agents which respond reactively to internal and external events by selecting the first-available plan. Priority between plans is hard-coded in the program, and so the reasons why a certain plan is preferred remain in the programmer’s mind. Recent works that attempt to include explicit preferences in BDI agents treat preferences essentially as a rationale for planning tasks to be performed at run-time, thus disrupting the reactive nature of agents. In this paper we propose a method to include declarative preferences (i.e. concerning states of affairs) in the agent program, and to use them in a manner that preserves reactivity. To achieve this, the plan prioritization step is performed offline, by (a) generating all possible outcomes of situated plan executions, (b) selecting a relevant subset of situation/outcomes couplings as representative summary for each plan, (c) sorting the plans by evaluating summaries through the agent’s preferences. The task of generating outcomes in several conditions is performed by translating the agent’s procedural knowledge to an ASP program using discrete-event calculus.

This short paper aims to unpack some of the assumptions underlying the “Policy and Investment Recommendation for Trustworthy AI” provided by the High-Level Expert Group on Artificial Intelligence (AI) appointed by the European Commission. It elaborates in particular on three aspects: on the technical-legal dimensions of trustworthy AI; on what we mean by AI; and on the impact of AI. The consequent analysis results in the identification, amongst others, of three recurrent simplifications, respectively concerning the definition of AI (sub-symbolic systems instead of “intelligent” informational processing systems), the interface between AI and institutions (neatly separated instead of continuity) and a plausible technological evolution (expecting a plateau instead of a potentially near-disruptive innovation).

Modern radio telescopes combine thousands of receivers, long-distance networks, large-scale compute hardware, and intricate software. Due to this complexity, failures occur relatively frequently. In this work, we propose novel use of unsupervised deep learning to diagnose system health for modern radio telescopes. The model is a convolutional variational autoencoder (VAE) that enables the projection of the high-dimensional time–frequency data to a low-dimensional prescriptive space. Using this projection, telescope operators are able to visually inspect failures thereby maintaining system health. We have trained and evaluated the performance of the VAE quantitatively in controlled experiments on simulated data from HERA. Moreover, we present a qualitative assessment of the model trained and tested on real LOFAR data. Through the use of a naïve SVM classifier on the projected synthesized data, we show that there is a trade-off between the dimensionality of the projection and the number of compounded features in a given spectrogram. The VAE and SVM combination scores between 65 per cent and 90 per cent accuracy depending on the number of features in a given input. Finally, we show the prototype system-health-diagnostic web framework that integrates the evaluated model. The system is currently undergoing testing at the ASTRON observatory.

Advances in automation, communication, sensing and computation enable experimental scientific processes to generate data at increasingly great speeds and volumes. Research infrastructures are devised to take advantage of these data, providing advanced capabilities for acquisition, sharing, processing, and analysis; enabling advanced research and playing an ever-increasing role in the environmental and earth science research domain. The ENVRI community identified several recurring requirements in the development of environmental research infrastructures such as i) duplication of efforts to solve similar problems; ii) lack of standards to harmonise and accelerate development, and bring about interoperability; iii) a large number of data models and data information systems within the domain, and iv) a steep learning curve for integration complex research infrastructure systems. To address these challenges, the ENVRI community has developed and re-fined the Environmental Research Infrastructures Reference Model (ENVRI Reference Model or ENVRI RM), a modelling framework encoding this knowledge. The proposed modelling framework encompasses a language and a notation to describe the research domain, its systems and the requirements and challenges faced when implementing those systems. By adopting ENVRI RM as an integrative approach, the environmental research community can secure interoperability between infrastructures, enable reuse, share resources, experiences and common language, reduce unnecessary duplication of effort, and speed up the understanding of research infrastructure systems. This chapter provides a short introduction to the ENVRI RM.

E-Infrastructures play an increasingly important part in the provision of digital services to environmental researchers and other users. The availability of reliable networks, storage facilities, high performance and high throughput computers and associated middleware and services to ease their utilisation all contribute to enabling research and its exploitation. Their relevance, possible use and utilisation to date are described.

By effectively virtualizing operating systems and encapsulating necessary runtime contexts of software components and services, container technologies can significantly improve portability and efficiency for distributed application deployment. It flexibly extends virtual machine based cloud (Infrastructure-as-a-Service) as a much lighter virtual environment (container cluster) for agile application management. However, existing container management systems are not capable of handling concurrent requests efficiently, particularly for the underlying clusters with heterogeneous machines and the requested containers with multi-resource demands. In this paper, we propose an Enhanced Container Scheduler (ECSched) for efficiently scheduling concurrent container requests on heterogeneous clusters with multi-resource constraints. We formulate the container scheduling problem as a minimum cost flow problem (MCFP), and represent the container requirements using a specific graph data structure (flow network). ECSched affords flexibility in constructing the flow network based on a batch of concurrent requests, and performs the MCFP algorithm to schedule the concurrent requests in an online manner. We evaluate ECSched in different testbed clusters, and measure the scheduling overhead with large-scale simulations. The experimental results show that ECSched outperforms state-of-the-art container schedulers in container performance and resource efficiency, and only introduces a small and acceptable scheduling overhead in large-scale clusters.

To support the current trend of personalised medicine, a collaboration between different healthcare providers is increasingly vital. The main element is the ability to share data among all parties while abiding by a data sharing policy. The EPI (Enabling Personalised Intervention) project addresses the problem of personalised diagnosis by developing real-time monitoring services and digital health twins. The EPI services run over adaptive computing infrastructures which provide more flexibility to accommodate the different requests. This paper proposes the EPI framework to support these novel health services over programmable infrastructure. The framework works on aligning the parties’ ability to share data with the policy defined beforehand. We explain the approach by introducing the framework’s data sharing logic model. We define the formalism of the logic model to deduce feasible data movements between and possibly satisfy a data collaboration request. We reinforce the framework’s logic model by introducing the algorithms running on this federated system to simulate its workflow. We provide three healthcare use cases running on a typical EPI infrastructure. We evaluated our model according to three relevant parameters, performance, feasibility, and aggregation power, and we can conclude that our framework supports the required interoperability between the EPI partners.

The paper sketches and elaborates on a framework integrating agent-based modelling with advanced quantitative probabilistic methods based on copula theory. The motivation for such a framework is illustrated on a artificial market functioning with canonical asset pricing models, showing that dependencies specified by copulas can enrich agent-based models to capture both micro-macro effects (e.g. herding behaviour) and macro-level dependencies (e.g. asset price dependencies). In doing that, the paper highlights the theoretical challenges and extensions that would complete and improve the proposal as a tool for risk analysis.

Virtual Research Environments (VREs), also known as science gateways or virtual laboratories, assist researchers in data science by integrating tools for data discovery, data retrieval, workflow management and researcher collaboration, often coupled with a specific computing infrastructure. Recently, the push for better open data science has led to the creation of a variety of dedicated research infrastructures (RIs) that gather data and provide services to different research communities, all of which can be used independently of any specific VRE. There is therefore a need for generic VREs that can be coupled with the resources of many different RIs simultaneously, easily customised to the needs of specific communities. The resource metadata produced by these RIs rarely all adhere to any one standard or vocabulary however, making it difficult to search and discover resources independently of their providers without some translation into a common framework. Cross-RI search can be expedited by using mapping services that harvest RI-published metadata to build unified resource catalogues, but the development and operation of such services pose a number of challenges. In this paper, we discuss some of these challenges and look specifically at the VRE4EIC Metadata Portal, which uses X3ML mappings to build a single catalogue for describing data products and other resources provided by multiple RIs. The Metadata Portal was built in accordance to the e-VRE Reference Architecture, a microservice-based architecture for generic modular VREs, and uses the CERIF standard to structure its catalogued metadata. We consider the extent to which it addresses the challenges of cross-RI search, particularly in the environmental and earth science domain, and how it can be further augmented, for example to take advantage of linked vocabularies to provide more intelligent semantic search across multiple domains of discourse.

In both hardware and software, masking can represent an effective means of hardening an implementation against side-channel attack vectors such as Differential Power Analysis (DPA). Focusing on software, however, the use of masking can present various challenges: specifically, it often 1) requires significant effort to translate any theoretical security properties into practice, and, even then, 2) imposes a significant overhead in terms of efficiency. To address both challenges, this paper explores the use of an Instruction Set Extension (ISE) to support masking in software-based implementations of a range of (symmetric) cryptographic kernels including AES: we design, implement, and evaluate such an ISE, using RISC-V as the base ISA. Our ISE-supported first-order masked implementation of AES, for example, is an order of magnitude more efficient than a software-only alternative with respect to both execution latency and memory footprint; this renders it comparable to an unmasked implementation using the same metrics, but also first-order secure.

Time-critical applications, such as early warning systems or live event broadcasting, present particular challenges. They have hard limits on Quality of Service constraints that must be maintained, despite network fluctuations and varying peaks of load. Consequently, such applications must adapt elastically on-demand, and so must be capable of reconfiguring themselves, along with the underlying cloud infrastructure, to satisfy their constraints. Software engineering tools and methodologies currently do not support such a paradigm. In this paper, we describe a framework that has been designed to meet these objectives, as part of the EU SWITCH project. SWITCH offers a flexible co-programming architecture that provides an abstraction layer and an underlying infrastructure environment, which can help to both specify and support the life cycle of time-critical cloud native applications. We describe the architecture, design and implementation of the SWITCH components and describe how such tools are applied to three time-critical real-world use cases.

The infrastructure‐as‐a‐service (IaaS) model of cloud computing provides virtual infrastructure functions (VIFs), which allow application developers to flexibly provision suitable virtual machines' (VM) types and locations, and even configure the network connection for each VM. Because of the pay‐as‐you‐go business model, IaaS provides an elastic way to operate applications on demand. However, in current cloud applications DevOps (software development and operations) lifecycle, the VM provisioning steps mainly rely on manually leveraging these VIFs. Moreover, these functions cannot be programmatically embedded into the application logic to control the infrastructure at runtime. Especially, the vendor lock‐in issue, which different clouds provide different VIFs, also enlarges this gap between the cloud infrastructure management and application operation. To mitigate this gap, we designed and implemented a framework, CloudsStorm, which enables developers to easily leverage VIFs of different clouds and program them into their cloud applications. To be specific, CloudsStorm empowers applications with infrastructure programmability at design‐level, infrastructure‐level, and application‐level. CloudsStorm also provides two infrastructure controlling modes, ie, active and passive mode, for applications at runtime. Besides, case studies about operating task‐based and big data applications on clouds show that the monetary cost is significantly reduced through the seamless and on‐demand infrastructure management provided by CloudsStorm. Finally, the scaling and recovery operation evaluations of CloudsStorm are performed to show its controlling performance. Compared with other tools, ie, “jcloud” and “cloudinit.d”, the scaling and provisioning performance evaluations demonstrate that CloudsStorm can achieve at least 10% efficiency improvement in our experiment settings.

Semantic annotation is a crucial part of achieving the vision of the Semantic Web and has long been a research topic among various communities. The most challenging problem in reaching the Semantic Web’s real potential is the gap between a large amount of unlabeled existing/new data and the limited annotation capability available. To resolve this problem, numerous works have been carried out to increase the degree of automation of semantic annotation from manual to semi-automatic to fully automatic. The richness of these works has been well-investigated by numerous surveys focusing on different aspects of the problem. However, a comprehensive survey targeting unsupervised approaches for semantic annotation is still missing and is urgently needed. To better understand the state-of-the-art of semantic annotation in the textual domain adopting unsupervised approaches, this article investigates existing literature and presents a survey to answer three research questions: (1) To what extent can semantic annotation be performed in a fully automatic manner by using an unsupervised way? (2) What kind of unsupervised approaches for semantic annotation already exist in literature? (3) What characteristics and relationships do these approaches have? In contrast to existing surveys, this article helps the reader get an insight into the state-of-art of semantic annotation using unsupervised approaches. While examining the literature, this article also addresses the inconsistency in the terminology used in the literature to describe the various semantic annotation tools’ degree of automation and provides more consistent terminology. Based on this, a uniform summary of the degree of automation of the many semantic annotation tools that were previously investigated can now be presented.

Future distributed scientific applications will rely on containerization for data handling and processing. Likewise, emerging digital marketplaces will be built using these technologies. In particular, support for multi-tenancy will be a major requirement. The question is whether container networking is already mature enough to support the level of usability required in these environments.With the work we present here we set out to experiment and evaluate three novel technologies that support addressing and filtering in container overlays: EVPN, ILA and Cilium/eBPF. We show that it is possible to integrate ILA and eBPF to support dynamic connections between containers. We describe performance testing of filtering operations with Cilium/eBPF and the initial demonstration of ILA showcased during the 2017 SuperComputing conference.All the experiments presented in this article identify the basic building blocks for the much needed container networking multi-tenancy.

Purpose The purpose of this paper is to boost multidisciplinary research by the building of an integrated catalogue or research assets metadata. Such an integrated catalogue should enable researchers to solve problems or analyse phenomena that require a view across several scientific domains. Design/methodology/approach There are two main approaches for integrating metadata catalogues provided by different e-science research infrastructures (e-RIs): centralised and distributed. The authors decided to implement a central metadata catalogue that describes, provides access to and records actions on the assets of a number of e-RIs participating in the system. The authors chose the CERIF data model for description of assets available via the integrated catalogue. Analysis of popular metadata formats used in e-RIs has been conducted, and mappings between popular formats and the CERIF data model have been defined using an XML-based tool for description and automatic execution of mappings. Findings An integrated catalogue of research assets metadata has been created. Metadata from e-RIs supporting Dublin Core, ISO 19139, DCAT-AP, EPOS-DCAT-AP, OIL-E and CKAN formats can be integrated into the catalogue. Metadata are stored in CERIF RDF in the integrated catalogue. A web portal for searching this catalogue has been implemented. Research limitations/implications Only five formats are supported at this moment. However, description of mappings between other source formats and the target CERIF format can be defined in the future using the 3M tool, an XML-based tool for describing X3ML mappings that can then be automatically executed on XML metadata records. The approach and best practices described in this paper can thus be applied in future mappings between other metadata formats. Practical implications The integrated catalogue is a part of the eVRE prototype, which is a result of the VRE4EIC H2020 project. Social implications The integrated catalogue should boost the performance of multi-disciplinary research; thus it has the potential to enhance the practice of data science and so contribute to an increasingly knowledge-based society. Originality/value A novel approach for creation of the integrated catalogue has been defined and implemented. The approach includes definition of mappings between various formats. Defined mappings are effective and shareable.

Diversified ranking on graphs ( DRG ) is an important and challenging issue in researching graph data mining. Traditionally, this problem is modeled by a submodular optimization objective, and solved by applying a cardinality constrained monotone submodular maximization. However, the existing submodular objectives do not directly capture the dis-similarity over pairs of nodes, while most of algorithms cannot easily take full advantage of the power of a distributed cluster computing platform, such as Spark, to significantly promote the efficiency of algorithms. To overcome the deficiencies of existing approaches, in this paper, a generalized distance metric based on a subadditive set function over the symmetry difference of neighbors of pairs of nodes is introduced to capture the pairwise dis-similarity over pairs of nodes. In our approach, DRG is formulated as a Max-Sum k-dispersion problem with metrical edge weights, which is NP-hard, in association with the proposed distance metric, a centralized linear time 2-approximation algorithm GA is then developed to significantly solve the problem of DRG . Moreover, we develop a highly parallelizable algorithm for DRG , which can be easily implemented in MapReduce style parallel computation models using GA as a basic reducer. Finally, extensive experiments are conducted on real network datasets to verify the effectiveness and efficiency of our proposed approaches.

In the last few years, the field of lightweight cryptography has seen an influx in the number of block ciphers and hash functions being proposed. In the past there have been numerous papers that have looked at circuit level implementation of block ciphers with respect to lightweight metrics like area power and energy. In the paper by Banik et al. (SAC‘15), for example, by studying the energy consumption model of a CMOS gate, it was shown that the energy consumed per cycle during the encryption operation of an r-round unrolled architecture of any block cipher is a quadratic function in r. However, most of these explorative works were at a gate level, in which a circuit synthesizer would construct a circuit using gates from a standard cell library, and the area power and energy would be estimated by estimating the switching statistics of the nodes in the circuit. Since only a part of the EDA design flow was done, it did not account for issues that might arise when the circuit is finally mapped into silicon post route. Metrics like area, power and energy would need to be re-estimated due to the effect of the parasitics introduced in the circuit by the connecting wires, nodes and interconnects. In this paper, we look to plug this very gap in literature by re-examining the designs of lightweight block ciphers with respect to their performances after completing the placement and routing process. This is a timely exercise to do since three of the block ciphers we analyze in the paper are used in around 13 of the 32 candidates in the second round of the NIST lightweight competition being conducted currently.

There has been a number of reports showing evidence that human movement behaviour follows patterns resembling Levy walks. These studies focus on the foraging patterns of rural humans and human hunter-gatherers. Here, we investigate motion patterns of visitors of a large dance event in the Johan Cruijff ArenA football stadium in Amsterdam. We find intermittent, persistent motion patterns. Using a path segmentation algorithm, we measure displacements (step lengths), and movement durations. We explore an alternative approach in the analysis of the movement tracks to overcome the limitations set by the bounded, concentric space of the building. Displacement distributions resulting from our alternative model deviate from the exponential and are best fit by a stretched exponential distribution. To further investigate the motion, we look at the mean-square displacement and autocorrelation of the turning angles. Although we find no evidence of Levy walks, individuals move with directional persistence and superdiffusively up to a scale set by the size of the stadium.

Analogies are common part of human life; our ability to handle them is critical in problem solving, humor, metaphors and argumentation. This paper introduces a method to solve string-based (symbolic) analogies based on hybrid inferential process integrating Structural Information Theory—a framework used to predict phenomena of perceptual organization—with some metric-based processing. Results are discussed against two empirical experiments, one of which conducted along this work, together with the development of a Python version of the SIT encoding algorithm PISA.

RISC-V has emerged as a viable solution on academia and industry. However, to use open source hardware for safety-critical applications, we need a deep understanding of the way in which well established mechanisms for testing and reliability could be integrated and deployed on the RISCV ecosystem, and we need a clear knowledge on how such an ecosystem can be leveraged to improve security. This paper includes four contributions presenting the potential of RISC-V in security research, the way in which RISC-V can be hardened against power analysis attacks, how to implement, using RISCV, software and hardware/software solutions for dual core lock step, and how to perform system-level testing in the RISC-V ecosystem.

In this work we provide an analysis of some issues arising with geometrical representations of a family of deontic and potestative relations that can be classified as Hohfeldian modalities, traditionally illustrated on two diagrams, the Hohfeldian squares. Our main target is the lack of symmetry to be found in various formal accounts by drawing analogies with the square of opposition for alethic modalities. We argue that one should rather rely on an analogy with the alethic hexagon of opposition and exploit the notions of contingency and absoluteness in order to restore the symmetry of Hohfeldian modalities in accordance to the diagrams presented by Hohfeld. Interestingly, the investigation unveils three potestative squares defined at different levels of granularity (force, outcome and change) and allows us to further elaborate on the connections between deontic and potestative relations.

Data manipulation is often considered a serious problem in industrial applications as data tampering can lead to inaccurate financial reporting or even a corporate security crisis. A correct representation of company data is essential for the companies’ core business processes and is requested by governments and investors. However, the current solution, third-party auditing, is expensive and cannot be fully trusted. In this paper, we present the Data Warehouse Decentralized Integrity Model (DWH-DIM) to validate the integrity of the data warehouse and replace the current process. To address the challenge that the existing distributed integrity verification models cannot handle GDPR and are limited by scalability, our model uses a distributed file system to store attributes that can be used for the integrity verification task. The blockchain further confirms the authenticity of the files. Based on the proposed model, we present a detailed implementation of the DWH-DIM tool. The implementation is tested with a use case and several benchmarks. Experimental results demonstrate that our proposed model is feasible and meets the requirement for certificate warehouse data.

Social media applications are essential for next generation connectivity. Today, social media are centralized platforms with a single proprietary organization controlling the network and posing critical trust and governance issues over the created and propagated content. The ARTICONF project [ 1 ] funded by the European Union’s Horizon 2020 program researches a decentralized social media platform based on a novel set of trustworthy, resilient and globally sustainable tools that address privacy, robustness and autonomy-related promises that proprietary social media platforms have failed to deliver so far. This paper presents the ARTICONF approach to a car-sharing decentralized application (DApp) use case, as a new collaborative peer-to-peer model providing an alternative solution to private car ownership. We describe a prototype implementation of the car-sharing social media DApp and illustrate through real snapshots how the different ARTICONF tools support it in a simulated scenario.

What eventually determines the semantics of algorithmic decision-making in not the program artefact, nor—if applicable—the data used to create it, but the preparatory (enabling) and consequent (enabled) practices holding in the environment (computational and human) in which such algorithmic procedure is embedded. The notion of responsibility captures a very similar construct: in all human societies actions are evaluated in terms of the consequences they could reasonably cause, and of the reasons that motivate them. But to what extent does this function exist in computational systems? The paper aims to sketch links between several of the approaches and concepts proposed for responsible computing, from AI to networking, identifying gaps and possible directions for operationalization.

International audience; Post-Quantum Cryptography (PQC) will become soon the standard for many systems of the future. With the advent of quantum computers, all encrypted communications based on traditional asymmetric cryptography (e.g., RSA, ECC) will become insecure. The definition the PQC standards is an on going process proceeding at a fast pace, involving new and largely unexplored cryptographic primitives. For this reason, the design of hardware implementations of PQC algorithms is still under study. In this paper, we introduce the fundamentals of PQC, with a focus on lattice-based cryptography and its hardware security issues, namely side-channel and fault-based attacks. Then, we focus on isogeny-based cryptography and the SIKE algorithm. We highlight the importance of fault-tolerant design choices through the presentation of a fault attack, based on the electromagnetic injection of transient faults, targeting this cryptographic primitive. Finally, we show an interesting idea that starts from the observation that some PQC algorithms have an intrinsic probabilistic behavior. We argue that this characteristic is a clear opportunity that paves the way for the application of approximate (or inexact) computing to the implementation of PQC cryptography.

A smart contract is code deployed in a blockchain environment, or the source code from which such code was compiled.

What eventually determines the semantics of algorithmic decision-making is not the program artefact, nor—if applicable—the data used to create it, but the preparatory (enabling) and consequent (enabled) practices holding in the environment (computational and human) in which such algorithmic procedure is embedded. The notion of responsibility captures a very similar construct: in all human societies actions are evaluated in terms of the consequences they could reasonably cause, and of the reasons that motivate them. But to what extent does this function exist in computational systems? The paper aims to sketch links between several of the approaches and concepts proposed for responsible computing, from AI to networking, identifying gaps and possible directions for operationalization.

Modern application-specific integrated circuits (ASICs) are increasingly employed in domains where they must fulfill security requirements. Traditional ASIC design flows include numerous steps to ensure the correctness of a circuit and its freedom from manufacturing defects, but they do not cover security vulnerabilities. In this paper, we show how to leverage state-of-the-art electronic design automation (EDA) tools to validate the resistance of a circuit against fault injection attacks in early design steps (before fabrication). While the approach is generic, we demonstrate it on a specific physical attack vector: Fault Sensitivity Analysis (FSA). We show how existing tools (especially for logic and timing simulation) can be extended by custom scripts to assess the vulnerability of an implementation to such attacks.

Performance requirements in many applications can often be modelled as constraints related to time, for example, the span of data processing for disaster early warning [1], latency in live event broadcasting [2], and jitter during audio/video conferences [3]. These time constraints are often treated either in an “as fast as possible” manner, such as sensitive latencies in high-performance computing or communication tasks, or in a “timeliness” way where tasks have to be finished within a given window in real-time systems, as classified in [4]. To meet the required time constraints, one has to carefully analyse time constraints, engineer and integrate system components, and optimise the scheduling for computing and communication tasks. The development of a time-critical application is thus time-consuming and costly. During the past decades, the infrastructure technologies of computing, storage and networking have made tremendous progress. Besides the capacity and performance of physical devices, the virtualisation technologies offer effective resource management and isolation at different levels, such as Java Virtual Machines at the application level, Dockers at the operating system level, and Virtual Machines at the whole system level. Moreover, the network embedding [5] and software-defined networking [6] provide network-level virtualisation and control that enable a new paradigm of infrastructure, where infrastructure resources can be virtualised, isolated, and dynamically customised based on application needs. The software-defined infrastructures, including Cloud, Fog, Edge, software-defined networking and network function virtualisation, emerge nowadays as new environments for distributed applications with time-critical application requirements, but also face challenges in effectively utilising the advanced infrastructure features in system engineering and dynamic control. This special issue on “time-critical applications and software-defined infrastructures” focuses on practical aspects of the design, development, customisation and performance-oriented operation of such applications for Clouds and other distributed environments.

Recommender systems should be able to handle highly sparse training data that continues to change over time. Among the many solutions, Ant Colony Optimization, as a kind of optimization algorithm modeled on the actions of an ant colony, enjoys the favorable characteristic of being optimal, which has not been easily achieved by other kinds of algorithms. A recent work adopting genetic optimization proposes a collaborative filtering scheme: Ant Collaborative Filtering (ACF), which models the pheromone of ants for a recommender system in two ways: (1) use the pheromone exchange to model the ratings given by users with respect to items, (2) use the evaporation of existing pheromone to model the evolution of users&rsquo, preference change over time. This mechanism helps to identify the users and the items most related, even in the case of sparsity, and can capture the drift of user preferences over time. However, it reveals that many users share the same preference over items, which means it is not necessary to initialize each user with a unique type of pheromone, as was done with the ACF. Regarding the sparsity problem, this work takes one step further to improve the Ant Collaborative Filtering&rsquo, s performance by adding a clustering step in the initialization phase to reduce the dimension of the rate matrix, which leads to the results that K&lt, &lt, users, where K is the number of clusters, which stands for the maximum number of types of pheromone carried by all users. We call this revised version the Improved Ant Collaborative Filtering (IACF). Experiments are conducted on larger datasets, compared with the previous work, based on three typical recommender systems: (1) movie recommendations, (2) music recommendations, and (3) book recommendations. For movie recommendation, a larger dataset, MoviesLens 10M, was used, instead of MoviesLens 1M. For book recommendation and music recommendation, we used a new dataset that has a much larger size of samples from Douban and NetEase. The results illustrate that our IACF algorithm can better deal with practical recommendation scenarios that handle sparse dataset.

Approximate computing is an architectural paradigm where limited and controlled errors during computation are tolerated. Thanks to approximation, circuits can be faster, more compact, and consume less power. Security aspects of these circuits are however largely unexplored. In this paper we focus on the problem of side channel attacks, and we discuss how they can be different when carried out in approximated circuits. We conclude our work highlighting challenges and possible research directions in this area.

The increasing volume of data being produced, curated, and made available by research infrastructures in the environmental science domain require services that are able to optimize the delivery and staging of data for researchers and other users of scientific data. Specialized data services for managing data life cycle, for creating and delivering data products, and for customized data processing and analysis all play a crucial role in how these research infrastructures serve their communities, and many of these activities are time‐critical—needing to be carried out frequently within specific time windows. We describe our experiences identifying the time‐critical requirements of environmental scientists making use of computational research support environments. We present a microservice‐based infrastructure optimization suite, the Dynamic Real‐Time Infrastructure Planner, used for constructing virtual infrastructures for research applications on demand. We provide a case study whereby our suite is used to optimize runtime service quality for a data subscription service provided by the Euro‐Argo using EGI Federated Cloud and EUDAT's B2SAFE services, and to consider how such a case study relates to other application scenarios.

Malleable applications may run with varying numbers of threads, and thus on varying numbers of cores, while the precise number of threads is irrelevant for the program logic. Malleability is a common property in data-parallel array processing. With ever growing core counts we are increasingly faced with the problem of how to choose the best number of threads. We propose a compiler-directed, almost automatic tuning approach for the functional array processing language SaC. Our approach consists of an offline training phase during which compiler-instrumented application code systematically explores the design space and accumulates a persistent database of profiling data. When generating production code our compiler consults this database and augments each data-parallel operation with a recommendation table. Based on these recommendation tables the runtime system chooses the number of threads individually for each data-parallel operation. With energy/power efficiency becoming an ever greater concern, we explicitly distinguish between two application scenarios: aiming at best possible performance or aiming at a beneficial trade-off between performance and resource investment.

The next generation of supercomputers will break the exascale barrier. Soon we will have systems capable of at least one quintillion (billion billion) floating-point operations per second (10 18 FLOPS). Tremendous amounts of work have been invested into identifying and overcoming the challenges of the exascale era. In this work, we present an overview of these efforts and provide insight into the important trends, developments, and exciting research opportunities in exascale computing. We use a three-stage approach in which we (1) discuss various exascale landmark studies, (2) use data-driven techniques to analyze the large collection of related literature, and (3) discuss eight research areas in depth based on influential articles. Overall, we observe that great advancements have been made in tackling the two primary exascale challenges: energy efficiency and fault tolerance. However, as we look forward, we still foresee two major concerns: the lack of suitable programming tools and the growing gap between processor performance and data bandwidth (i.e., memory, storage, networks). Although we will certainly reach exascale soon, without additional research, these issues could potentially limit the applicability of exascale computing.

Hierarchical scheduling provides a means of composing multiple real-time applications onto a single processor such that the temporal requirements of each application are met. This has become a popular technique in industry as it allows applications from multiple vendors as well as legacy applications to co-exist in isolation on the same platform. However, performance enhancing features such as caches mean that one application can interfere with another by evicting blocks from cache that were in use by another application, violating the requirement of temporal isolation. In this paper, we present some initial analysis that bounds the additional delay due to blocks being evicted from cache by other applications in a system using hierarchical scheduling when using a local EDF scheduler.

Experience has shown that a priori created static resource allocation plans are vulnerable to runtime deviations and hence often become uneconomic or highly exceed a predefined soft deadline. The assumption of constant task execution times during allocation planning is even more unlikely in a cloud environment where virtualized resources vary in performance. Revising the initially created resource allocation plan at runtime allows the scheduler to react on deviations between planning and execution. Such an adaptive rescheduling of a many‐task application workflow is only feasible, when the planning time can be handled efficiently at runtime. In this paper, we present the static low‐complexity resource allocation planning algorithm (LCP) applicable to efficiently schedule many‐task scientific application workflows on cloud resources of different capabilities. The benefits of the presented algorithm are benchmarked against alternative approaches. The benchmark results show that LCP is not only able to compete against higher complexity algorithms in terms of planned costs and planned makespan but also outperforms them significantly by magnitudes of 2 to 160 in terms of required planning time. Hence, LCP is superior in terms of practical usability where low planning time is essential such as in our targeted online rescheduling scenario.

In this thesis we show that it is possible to build Secure Autonomous Response NETworks (SARNETs); networks that are capable of protecting themselves against attacks. The work describes how control software that utilises Software Defined Networking and Network Function Virtualisation can protect the services in the overlay network. We explain how we combine data from different information sources to improve attack classification and we define metrics that can be used to evaluate defences against attacks in single as well as in multi-domain environments. Finally, we show that multi-domain defence orchestration benefits from considering trust between the collaborating domains. The proof-of-concepts and contributions in this thesis open up new research challenges and introduce ideas that can help to build a more secure and reliable Internet.

The paper introduces an Agent-Oriented Programming (AOP) framework based on the Belief-Desire-Intention (BDI) model of agency. The novelty of this framework is in relying on the Actor model, instantiating each intentional agent as an autonomous micro-system run by actors. The working hypothesis behind this choice is that defining the agents via actors results in a more fine-grained modular architecture and that the execution of agent-oriented programs is enhanced (in scalability as well as in performance) by relying on robust implementations of Actor models such as Akka. The framework is benchmarked and analyzed quantitatively and qualitatively against three other AOP frameworks: Jason, ASTRA and Sarl.

Environmental research infrastructures (RIs) support their respective re-search communities by integrating large-scale sensor/observation networks with data curation and management services, analytical tools and common operational policies. These RIs are developed as service pillars for intra- and interdisciplinary research; however, comprehension of the complex, interconnected aspects of the Earth’s ecosystem increasingly requires that re-searchers conduct their experiments across infrastructure boundaries. Con-sequently, almost all data-related activities within these infrastructures, from data capture to data usage, need to be designed to be broadly interoperable in order to enable real interdisciplinary innovation and to improve service offerings through the development of common services. To address these interoperability challenges as they relate to the design, implementation and operation of environmental RIs, a Reference Model guided engineering approach was proposed and has been used in the context of the ENVRI cluster of RIs. In this chapter, we will discuss how the approach combines the ENVRI Reference Model with the practices of Agile systems development to design common data management services and to tackle the dynamic requirements of research infrastructures.

Machine learning techniques have significantly changed our lives. They helped improving our everyday routines, but they also demonstrated to be an extremely helpful tool for more advanced and complex applications. However, the implications of hardware security problems under a massive diffusion of machine learning techniques are still to be completely understood. This paper first highlights novel applications of machine learning for hardware security, such as evaluation of post quantum cryptography hardware and extraction of physically unclonable functions from neural networks. Later, practical model extraction attack based on electromagnetic side-channel measurements are demonstrated followed by a discussion of strategies to protect proprietary models by watermarking them.

International audience; In the last two decades we have witnessed a massive development of technologies (both hardware and software) which have enabled the creation of billions of connected devices. These devices are nowadays used in a very wide range of applications, and they all contain different types of valuable assets, which have been the target of an increasing number of cyber attacks. Both scientific and industrial communities have focused their attention to implement new design processes to reduce the risk of cybersecurity breaches. This paper includes two different contributions in the field of hardware security and privacy

In game development, the maximum number of game design iterations determines the achievable quality. This thesis explores what informs the design and construction of good games in order to help speed-up game development processes, and create better games more quickly. On the one hand, we study how Domain-Specific Languages (DSLs) can help automate game design by offering developers and designers abstractions and notations that raise their productivity, reduce iteration times, and improve the quality of player experiences and a game’s source code. On the other hand, we explore how generic language technology can be leveraged and developed, in particular for constructing DSLs for live programming and automating game design. Chapter 2 presents a systematic mapping study on the state-of-the-art of ‘Languages of Games and Play’. We survey language-centric approaches for tackling challenges and solving problems related to game design and development. Our map provides a good starting point for anyone who wishes to learn more about the topic. Chapters 3, 4 and 5 describe Micro-Machinations, a DSL for game-economic mechanics. We propose techniques for its analysis, live programming and pattern-based generation. Chapter 6 demonstrates how generic language technology can be developed for constructing DSLs for live programming. We contribute two techniques: textual model differencing and run-time model patching. Finally, Chapter 7 reports preliminary results on improving tool support for automated game level design.

Computational systems are traditionally approached from control-oriented perspectives; however, as soon as we move from central- ized to decentralized computational infrastructures, direct control needs to be replaced by distributed coordination mechanisms that are on par with institutional constructs observable in human societies (contracts, agreements, enforcement mechanisms, etc.). This paper presents a for- malization of Hohfeld's framework building upon a logic whose language includes primitive operators of ability and parametric deontic operators. The proposal is meant to highlight the fundamental interaction between deontic and potestative concepts and contains proofs of soundness and completeness with respect to a class of relational models.

This book summarises the latest developments on data management in the EU H2020 ENVRIplus project, which brought together several environmental and earth science research infrastructures into a single community. It provides readers with a systematic overview of the common challenges faced by research infrastructures and how a ‘reference model guided’ engineering approach can be used to achieve greater interoperability among such infrastructures in the environmental and earth sciences.The research problems behind environmental and societal challenges such as cli-mate change, food security and natural disasters are intrinsically interdisciplinary. Modelling these processes individually is difficult enough, but modelling their inter-actions is another order of complexity entirely. Scientists are challenged to collaborate across conventional disciplinary boundaries, but must first discover and extract data dispersed across many different sources and in many different formats. Effective re-search support environments are needed for various user-centralised research activities, from formulating research problems to designing experiments, discovering data and services, executing workflows, and analysing then publishing the final results. Such support environments also have to manage research data during their entire lifecycle, throughout the phases of data acquisition, curation, publication, processing and use. Moreover, support environments must support the management of underlying infra-structure resources for computing, storage and networking. In this ecosystem, research infrastructure (RI) is an important form of supportive environment that bridges the gap between the curation of research data and user-centred scientific activity, and also between research data and the underlying physical infrastructure. It brings together facilities, resources and services used by the scientific community to conduct research, establish best practices for science, and foster innovation. The book presents the design, development, deployment, operation and use of re-search infrastructures as 20 chapters via five parts. The part one provides an overview of the state of the art of research infrastructure and relevant e-Infrastructure technolo-gies, part two discusses the reference model guided engineering approach, the third part presents the software and tools developed for common data management chal-lenges, the fourth part demonstrates the software via several use cases, and the last part discusses the sustainability and future directions. The main readers of the book will be developers, managers, operators and potential users of research infrastructures in environmental and earth sciences. The book will provide RI data managers in environmental and earth sciences with a common onto-logical framework and facilities for modelling data management requirements, and practical data management guidelines during entire research life-cycle. It will provide RI stakeholders with very practical case studies on RI architecture design, service interoperability, and system-level environmental research. The book can also be a textbook for the training young researchers and data managers data management skills, RI service development and operation practices, and using RIs for data-centric research. The development of the book has been greatly supported by the project coordinator and all partners, in particular those who are involved in the data for science theme. We thank all the authors who contribute to the book chapters, and editorial board members who provide a detailed review and valuable feedback on the content. With-out their support, this book would not have been possible.

Large-scale science instruments, such as the distributed radio telescope LOFAR, show that we are in an era of data-intensive scientific discovery. Such instruments rely critically on significant computing resources, both hardware and software, to do science. Considering limited science budgets, and the small fraction of these that can be dedicated to compute hardware and software, there is a strong and obvious desire for low-cost computing. However, optimising for cost is only part of the equation; the value potential over the lifetime of the solution should also be taken into account. Using a tangible example, compute hardware, we introduce a conceptual model to approximate the lifetime relative science value of such a system. While the introduced model is not intended to result in a numeric value for merit, it does enumerate some components that define this metric. The intent of this paper is to show how compute system related design and procurement decisions in data-intensive science projects should be weighed and valued. By using both total cost and science value as a driver, the science output per invested Euro is maximised. With a number of case studies, focused on computing applications in radio astronomy past, present and future, we show that the hardware-based analysis can be, and has been, applied more broadly.

Environmental research infrastructures aim to provide scientists with facilities, resources and services to enable scientists to effectively perform advanced research. When addressing societal challenges such as climate change and pollution, scientists usually need data, models and methods from different domains to tackle the complexity of the complete environ-mental system. Research infrastructures are thus required to enable all data, including services, products, and virtual research environments is FAIR for research communities: Findable, Accessible, Interoperable and Reusable. In this last chapter, we conclude and identify future challenges in research infrastructure operation, user support, interoperability, and future evolution.