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With an growing number of web services available on the Internet, an increasing burden is imposed on the use and management of service repository. Service clustering has been employed to facilitate a wide range of service-oriented tasks, such as service discovery, selection, composition and recommendation. Conventional approaches have been proposed to cluster web services by using explicit features, including syntactic features contained in service descriptions or semantic features extracted by probabilistic topic models. However, service implicit features are ignored and have yet to be properly explored and leveraged. To this end, we propose a novel heuristics-based framework DeepWSC for web service clustering. It integrates deep semantic features extracted from service descriptions by an improved recurrent convolutional neural network and service composability features obtained from service invocation relationships by a signed graph convolutional network, to jointly generate integrated implicit features for web service clustering. Extensive experiments are conducted on 8,459 real-world web services. The experiment results demonstrate that DeepWSC outperforms state-of-the-art approaches for web service clustering in terms of multiple evaluation metrics.

Tangible controls-especially sliders and rotary knobs-have been explored in a wide range of interactive applications for desktop and immersive environments. Studies have shown that they support greater precision and provide proprioceptive benefits, such as support for eyes-free interaction. However, such controls tend to be expressly designed for specific applications. We draw inspiration from a bespoke controller for immersive data visualisation, but decompose this design into a simple, wireless, composable unit featuring two actuated sliders and a rotary encoder. Through these controller units, we explore the interaction opportunities around actuated sliders; supporting precise selection, infinite scrolling, adaptive data representations, and rich haptic feedback; all within a mode-less interaction space. We demonstrate the controllers' use for simple, ad hoc desktop interaction,before moving on to more complex, multi-dimensional interactions in VR and AR. We show that the flexibility and composability of these actuated controllers provides an emergent design space which covers the range of interactive dynamics for visual analysis. In a user study involving pairs performing collaborative visual analysis tasks in mixed-reality, our participants were able to easily compose rich visualisations, make insights and discuss their findings.

International audience; The design of glitch-resistant higher-order masking schemes is an important challenge in cryptographic engineering. A recent work by Moos et al. (CHES 2019) showed that most published schemes (and all efficient ones) exhibit local or composability flaws at high security orders, leaving a critical gap in the literature on hardware masking. In this paper, we first extend the simulatability framework of Belaïd et al. (EUROCRYPT 2016) and prove that a compositional strategy that is correct without glitches remains valid with glitches. We then use this extended framework to prove the first masked gadgets that enable trivial composition with glitches at arbitrary orders. We show that the resulting "Hardware Private Circuits" approach the implementation efficiency of previous (flawed) schemes. We finally investigate how trivial composition can serve as a basis for a tool that allows verifying full masked hardware implementations (e.g., of complete block ciphers) at any security order from their HDL code. As side products, we improve the randomness complexity of the best published refreshing gadgets, show that some S-box representations allow latency reductions and confirm practical claims based on implementation results.

Differential Privacy (DP) formalizes privacy in mathematical terms and provides a robust concept for privacy protection. DIfferentially Private Data Synthesis (DIPS) techniques produce and release synthetic individual-level data in the DP framework. One key challenge to develop DIPS methods is the preservation of the statistical utility of synthetic data, especially in high-dimensional settings. We propose a new DIPS approach, STatistical Election to Partition Sequentially (STEPS) that partitions data by attributes according to their importance ranks according to either a practical or statistical importance measure. STEPS aims to achieve better original information preservation for the attributes with higher importance ranks and produce thus more useful synthetic data overall. We present an algorithm to implement the STEPS procedure and employ the privacy budget composability to ensure the overall privacy cost is controlled at the pre-specified value. We apply the STEPS procedure to both simulated data and the 2000–2012 Current Population Survey youth voter data. The results suggest STEPS can better preserve the population-level information and the original information for some analyses compared to PrivBayes, a modified Uniform histogram approach, and the flat Laplace sanitizer.

Byzantine fault-tolerant (BFT) state-machine replication makes it possible to design systems that are resilient against arbitrary faults, a requirement considered crucial for an increasing number of use cases such as permissioned blockchains, firewalls, and SCADA systems. Unfortunately, the strong fault-tolerance guarantees provided by BFT replication protocols come at the cost of a high complexity, which is why it is inherently difficult to correctly implement BFT systems in practice. This is all the more true with regard to the plethora of solutions and ideas that have been developed in recent years to improve performance, availability, or resource efficiency. This survey aims at facilitating the task of building BFT systems by presenting an overview of state-of-the-art techniques and analyzing their practical implications, for example, with respect to applicability and composability. In particular, this includes problems that arise in the context of concrete implementations, but which are often times passed over in literature. Starting with an in-depth discussion of the most important architectural building blocks of a BFT system (i.e., clients, agreement protocol, execution stage), the survey then focuses on selected approaches and mechanisms addressing specific tasks such as checkpointing and recovery.

In this work a platform-aware model-driven engineering process for building component-based embedded software systems using annotated analysis models is described. The process is supported by a framework, called MICOBS, that allows working with different component technologies and integrating different tools that, independently of the component technology, enable the analysis of non-functional properties based on the principles of composability and compositionality. An actor, called Framework Architect, is responsible for this integration. Three other actors take a relevant part in the analysis process. The Component Provider supplies the components, while the Component Tester is in charge of their validation. The latter also feeds MICOBS with the annotated analysis models that characterize the extra-functional properties of the components for the different platforms on which they can be deployed. The Application Architect uses these components to build new systems, performing the trade-off between different alternatives. At this stage, and in order to verify that the final system meets the extra-functional requirements, the Application Architect uses the reports generated by the integrated analysis tools. This process has been used to support the validation and verification of the on-board application software for the Instrument Control Unit of the Energetic Particle Detector of the Solar Orbiter mission.

We propose a novel framework for composing crowdsourced wireless energy services to satisfy users' energy requirements in a crowdsourced Internet of Things (IoT) environment. A new energy service model is designed to transform the harvested energy from IoT devices into crowdsourced services. We propose a new energy service composability model that considers the spatio-temporal aspects and the usage patterns of the IoT devices. A multiple local knapsack-based approach is developed to select an optimal set of partial energy services based on the deliverable energy capacity of IoT devices. We propose a heuristic-based composition approach using the temporal and energy capacity distributions of services. Experimental results demonstrate the effectiveness and efficiency of the proposed approach., 15 pages, accepted and to be published in the IEEE Transactions on Services Computing, 2020

The microservice architecture (MSA) differs fundamentally from the monolithic, layered architecture. The use of microservices provides a high degree of autonomy, composability, scalability, and fault-tolerance. MSA is regarded by many as a promising architecture for smart-city applications; however, a number of issues remain, including (1) the management of complex call relationships among microservices; (2) ensuring the quality of the overall software system even as new microservices are added and existing ones are modified, and (3) locating existing microservices that satisfy new requirements. In this paper, we propose a novel approach to the development of microservice-based systems, referred to as GSMART (Graph-based and Scenario-driven Microservice Analysis, Retrieval and Testing). GSMART enables the automatic generation of a “Service Dependency Graph (SDG)” by which to visualize and analyze dependency relationships between microservices as well as between services and scenarios. It also enables the automatic retrieval of test cases required for system changes to reduce the time and costs associated with regression testing. A microservice retrieval method using VSM and word2vec accelerates the development of new microservices tailored specifically to the needs of users based on user-provided scenarios. Experiment results demonstrate the feasibility, effectiveness, and efficiency of all of the main features of GSMART.

Smart CPSs (S-CPSs) have been evolving beyond what was identified by the traditional definitions of CPSs. The objective of our research is to investigate the concepts and implementations of reasoning processes for S-CPSs, and more specifically, the frameworks proposed for the fuzzy front end of their reasoning mechanisms. The objectives of the paper are: (i) to analyze the framework concepts and implementations of CPS, (ii) to review the literature concerning system-level reasoning and its enablers from the points of view of the processed knowledge, building awareness, reasoning mechanisms, decision making, and adaptation. Our findings are: (i) awareness and adaptation behaviors are considered as system-level smartness of S-CPSs that are not achieved by traditional design approaches; (ii) model-based and composability approaches insufficiently support the development of reasoning mechanisms for S-CPSs; (iii) frameworks for development of reasoning in S-CPS should support compositional design. Based on the conclusions above, we argue that coping with the challenges of compositionality requires both software-level integration and holistic fusion of knowledge by means of semantic transformations. This entails the need for a multi aspect framework that is able to capture at least conceptual, functional, architectural, informational, interoperation, and behavioral aspects. It needs further investigation if a compositionality enabling framework should appear in the form of a meta-framework (abstract) or in the form of a semantically integrated (concrete) framework. Highlights Smartness in CPSs is a holistic and synergistic behavioral characteristic. Complex mental representations are compositional. Compositionality is necessary for smart CPSs. Without a rigorous unifying framework, designing synthesis reasoning remains ad hoc.

The inforrmation age continues to transform the mechanics of integrating electric power devices and systems, from coordinated operations based purely on the physics of electric power engineering to an increasing blend of power with information and communication technology. Integrating electric system components is not just about attaching wires. It requires the connection of computer-based automation systems to associated sensing and communication equipment. The architectural impacts are significant. Well-considered and commonly held concepts, principles, and organizational structures continue to emerge to address the complexity of the integrated operational challenges that drive our society to expect more flexibility in configuring the electric power system, while simultaneously achieving greater efficiency, reliability, and resilience. Architectural concepts, such as modularity and composability, contribute to the creation of structures that enable the connection of power system equipment characterized by clearly defined interfaces consisting of physical and cyberlinks. The result of successful electric power system component connection is interoperation: the discipline that drives integration to be simple and reliable.

Agent-based modeling and simulation has been proved useful in a variety of different complex adaptive systems comprised of autonomous, interacting components. To resolve the shortcoming of current agent-based modeling frameworks with respect to decomposition and modularity, this paper presents a formal hierarchical modeling and simulation framework with three–level architecture to reduce ambiguity as well as improve clarity in the model definition. The bottom level is Component Model (CM), which implements some domain-specific support functionality, such as curve motion in the physical domain, intelligent decision-making in the cognitive domain, etc. The middle level is Agent Model (AM), which describes an agent which can react to the current situation by executing a sequence of CMs. The top level is System Model (SM), which defines that a CAS model consists of several AMs and also the interactions between these AMs. In the hierarchical architecture, one SM can be built up from lower-level models, which were linked in a loosely coupled fashion via an event-driven interface. We then analyze the reusability and composability of lower-level models of this hierarchical framework in a formalized way. To demonstrate the effectiveness of the proposed solution, we develop a graphical composite modelling tool named GraphSim, and the case study concerning two social dynamics system scenarios is also presented.

With the advent of the Internet of Things (IoT), Cloud-based Cyber-Physical Systems (C2PS) are becoming more prevalent in manufacturing systems. Verifying the security of C2PS is a challenging task due to the inclusion of newer entities such as mobile phones and IoT devices. Therefore, scalable methods for verifying security properties of manufacturing systems are paramount. This paper presents an approach to guarantee security of C2PS using concepts from Communicating Sequential Processes (CSP) to describe the security properties of cloud constructs. We prove that composability is an essential property for enhancing the security of the C2PS. Furthermore, it also provides good scalability to the system. We propose formalisms required for modelling security properties in the Industrial IoT-based manufacturing system. Using this formalism and Hoares’ logic we provide conditions for composing systems. Finally, we provide strong authentication conditions among entity pairs in C2PS based manufacturing systems. We demonstrate the message exchanges, methodology, and different steps required for verifying C2PS.

Publisher Copyright: © 2021 IEEE. We present work on structuring robotics simulation scenarios into components. Components can comprise simulation entities with a physical counterpart such as automated guided vehicles, drones, robots and machines. Components are typically further structured into sub-components, e.g., a robot arm or a gripper, and can be used to build digital twins. Different aspects for classifying components can be distinguished; here we concentrate on composability, performance and fidelity.In this paper, we are mainly concerned with components in the robotics simulation tool Gazebo used with Robot Operating System. Several structured scenario case studies are described. We investigate how these scenarios behave in Gazebo with respect to performance and fidelity for selected cases.

Logical Execution Time (LET) is a timed programming abstraction, which features predictable and composable timing. It has recently gained considerable attention in the automotive industry, where it was successfully applied to master the distribution of software applications on multi-core electronic control units. However, the LET abstraction in its conventional form is only valid within the scope of a single component. With the recent introduction of System-level Logical Execution Time (SL LET), the concept could be transferred to a system-wide scope. This article improves over a first paper on SL LET, by providing matured definitions and an extensive discussion of the concept. It also features a comprehensive evaluation exploring the impacts of SL LET with regard to design, verification, performance, and implementability. The evaluation goes far beyond the contexts in which LET was originally applied. Indeed, SL LET allows us to address many open challenges in the design and verification of complex embedded hardware/software systems addressing predictability, synchronization, composability, and extensibility. Furthermore, we investigate performance trade-offs, and we quantify implementation costs by providing an analysis of the additionally required buffers.

Representing the semantics of GUI screens and components is crucial to data-driven computational methods for modeling user-GUI interactions and mining GUI designs. Existing GUI semantic representations are limited to encoding either the textual content, the visual design and layout patterns, or the app contexts. Many representation techniques also require significant manual data annotation efforts. This paper presents Screen2Vec, a new self-supervised technique for generating representations in embedding vectors of GUI screens and components that encode all of the above GUI features without requiring manual annotation using the context of user interaction traces. Screen2Vec is inspired by the word embedding method Word2Vec, but uses a new two-layer pipeline informed by the structure of GUIs and interaction traces and incorporates screen- and app-specific metadata. Through several sample downstream tasks, we demonstrate Screen2Vec's key useful properties: representing between-screen similarity through nearest neighbors, composability, and capability to represent user tasks., Accepted to CHI Conference on Human Factors in Computing Systems (CHI 2021)

This article explores the metaphor of Science as provider of sharp images of ourenvironment, using the epistemological framework of Objective Cognitive Constructivism.These sharp images are conveyed by precise scientific hypotheses that, in turn, are encodedby mathematical equations. Furthermore, this article describes how such knowledge is pro-duced by a cyclic and recursive development, perfection and reinforcement process, leadingto the emergence of eigen-solutions characterized by the four essential properties of preci-sion, stability, separability and composability. Finally, this article discusses the role playedby ontology and metaphysics in the scientific production process, and in which sense theresulting knowledge can be considered objective.

Many-core platforms are rapidly expanding in various embedded areas as they provide the scalable computational power required to meet the ever-growing performance demands of embedded applications and systems. However, the huge design space of possible task mappings, the unpredictable workload dynamism, and the numerous non-functional requirements of applications in terms of timing, reliability, safety, and so forth. impose significant challenges when designing many-core systems. Hybrid Application Mapping (HAM) is an emerging class of design methodologies for many-core systems which address these challenges via an incremental (per-application) mapping scheme: The mapping process is divided into (i) a design-time Design Space Exploration (DSE) step per application to obtain a set of high-quality mapping options and (ii) a run-time system management step in which applications are launched dynamically (on demand) using the precomputed mappings. This paper provides an overview of HAM and the design methodologies developed in line with it. We introduce the basics of HAM and elaborate on the way it addresses the major challenges of application mapping in many-core systems. We provide an overview of the main challenges encountered when employing HAM and survey a collection of state-of-the-art techniques and methodologies proposed to address these challenges. We finally present an overview of open topics and challenges in HAM, provide a summary of emerging trends for addressing them particularly using machine learning, and outline possible future directions. While there exists a large body of HAM methodologies, the techniques studied in this paper are developed, to a large extent, within the scope of invasive computing. Invasive computing introduces resource awareness into applications and employs explicit resource reservation to enable incremental application mapping and dynamic system management., publishedVersion

Owing to the results of research in system science, artificial intelligence, and cognitive engineering, engineered systems are becoming more and more powered by knowledge. Complementing common-sense and scientific knowledge, system knowledge is maturing into a crucial productive asset. However, an overall theory of the knowledge of intellectualized systems does not exist. Some researchers suggest that time has come to establish a philosophically underpinned theoretical framework. This motion is seconded by the on-going intelligence revolution, in which artificial intelligence becomes a productive power, enabler of smart systems, and a strong transformer of the social life. The goal of this paper is to propose a skeleton of the needed generic theory of system knowledge (and a possible new branch of philosophical studies). The major assumption is that a significant part of the synthetic system knowledge (SSK) is &ldquo, symp&eacute, rasma&rdquo, that is, knowledge conjectured, inferred, constructed, or otherwise derived during the operation of systems. This part will become even more dominant in the future. Starting out from the above term, the paper suggests calling this theory &ldquo, rasmology&rdquo, Some specific domains of &ldquo, rasmological&rdquo, studies, such as (i) manifestations of SSK, (ii) mechanisms of generating SSK, (iii) dependability of SSK, (iv) operational power of SSK, (v) composability of SSK, and (vi) advancement of SSK, are identified. It is understood that the essence and status of SSK cannot be investigated without considering the related cognitive processes and technological enablers. The paper presents a number of open questions relevant for follow-up discussions.

Microservices architecture has become one of the most prominent software architectures in the software development processes due to its features such as scalability, maintainability, resilience, and composability. It allows developing business applications in a decentralized manner by dividing the important business logic into separate independent services. Digital certificates are used to verify the identity of microservices in most cases. However, the certificate authorities (CA) who issue the certificates to microservices cannot be trusted always since they can issue certificates without the consent of the relevant microservice. Nevertheless, existing implementations of certificate transparency are mostly centralized and has the vulnerability of the single point of failure. The distributed ledger technologies such as blockchain can be used to achieve decentralized nature in certificate transparency implementations. A blockchain-based decentralized certificate transparency system specified for microservices architecture is proposed in this paper to ensure secure communication among services. After the implementation and deployment in a cloud service, the system expressed average certificate querying time of 643 milliseconds along with the highly secured service provided.

We address energy-efficient placement of data and analytics components of composite analytics services on a wireless network to minimize execution-time energy consumption (computation and communication) subject to compute, storage and network resource constraints.We introduce an expressive analytics service hypergraph model for representing k-ary composability relationships (k ≥ 2) between various analytics and data components and leverage binary quadratic programming (BQP) to minimize the total energy consumption of a given placement of the analytics hypergraph nodes on the network subject to resource availability constraints. Then, after defining a potential energy functional Φ(•) to model the affinities of analytics components and network resources using analogs of attractive and repulsive forces in physics, we propose a decentralized Metropolis Monte Carlo (MMC) sampling method which seeks to minimize Φ by moving analytics and data on the network. Although Φ is non-convex, using a potential game formulation, we identify conditions under which the algorithm provably converges to a local minimum energy equilibrium placement configuration.Trace-based simulations of the placement of a deep-neural-network analytics service on a realistic wireless network show that for smaller problem instances our MMC algorithm yields placements with total energy within a small factor of BQP and more balanced workload distributions; for larger problems, it yields low-energy configurations while the BQP approach fails.

International audience; We propose a compositional specification theory for reason- ing about components that interact by synchronisation of input and out- put (I/O) actions, in which the specification of a component constrains the temporal ordering of interactions with the environment. Such a the- ory is motivated by the need to support composability of components, in addition to modelling environmental assumptions, and reasoning about run-time behaviour. Models can be specified operationally by means of I/O labelled transition systems augmented by an inconsistency predi- cate on states, or in a purely declarative manner by means of traces. We introduce a refinement preorder that supports safe-substitutivity of components. Our specification theory includes the operations of parallel composition for composing components at run-time, logical conjunction for independent development, and quotient for incremental development. We prove congruence properties of the operations and show correspon- dence between the operational and declarative frameworks.

Complex simulation systems often comprise multiple distributed simulators that need to interoperate and synchronize states and events. In many cases, the simulation logics which are developed by different teams with specific expertise, need to be integrated to build a complete simulation system. Thus, supporting composability and reusability of simulation functionalities with minimal integration and performance overhead is a challenging but required capability. Middleware for game engines are promising to realize both the modular and reusable development criteria as well as the high performance requirements, while data-centric publish/subscribe middleware can support seamless integration and synchronization of the distributed artifacts. However, differences in the level of abstraction at which these middleware operate and the semantic differences in their underlying ontologies make it hard and challenging for simulation application developers and system integrators to realize a complete, operational system. To that end this paper presents a model-driven approach to blending the two middleware, wherein the modeling capabilities provide intuitive and higher-level abstractions for developers to reason about the composition and validation of the complete system, and the generative capabilities address the inherent and accidental complexities incurred in reconciling the semantic differences between the gaming and pub/sub middleware. We present a concrete implementation of our approach and illustrate its use and performance results using simple use cases.

We propose an entanglement-based quantum bit string commitment protocol whose composability is proven in the random oracle model. This protocol has the additional property of preserving the privacy of the committed message. Even though this property is not resilient against man-in-the-middle attacks, this threat can be circumvented by considering that the parties communicate through an authenticated channel. The protocol remains secure (but not private) if we realize the random oracles as physical unclonable functions in the so-called bad PUF model with access before the opening phase., Comment: 8 pages, 4 figures

In recent years, misinformation on the Web has become increasingly rampant. The research community has responded by proposing systems and challenges, which are beginning to be useful for (various subtasks of) detecting misinformation. However, most proposed systems are based on deep learning techniques which are fine-tuned to specific domains, are difficult to interpret and produce results which are not machine readable. This limits their applicability and adoption as they can only be used by a select expert audience in very specific settings. In this paper we propose an architecture based on a core concept of Credibility Reviews (CRs) that can be used to build networks of distributed bots that collaborate for misinformation detection. The CRs serve as building blocks to compose graphs of (i) web content, (ii) existing credibility signals –fact-checked claims and reputation reviews of websites–, and (iii) automatically computed reviews. We implement this architecture on top of lightweight extensions to Schema.org and services providing generic NLP tasks for semantic similarity and stance detection. Evaluations on existing datasets of social-media posts, fake news and political speeches demonstrates several advantages over existing systems: extensibility, domain-independence, composability, explainability and transparency via provenance. Furthermore, we obtain competitive results without requiring finetuning and establish a new state of the art on the Clef’18 CheckThat! Factuality task.

Following the same trend of consumer electronics, safety-critical industries are starting to adopt Over-The-Air Software Updates (OTASU) on their embedded systems. The motivation behind this trend is twofold. On the one hand, OTASU offer several benefits to the product makers and users by improving or adding new functionality and services to the product without a complete redesign. On the other hand, the increasing connectivity trend makes OTASU a crucial cyber-security demand to download latest security patches. However, the application of OTASU in the safety-critical domain is not free of challenges, specially when considering the dramatic increase of software complexity and the resulting high computing performance demands. This is the mission of UP2DATE, a recently launched project funded within the European H2020 programme focused on new software update architectures for heterogeneous high-performance mixed-criticality systems. This paper gives an overview of UP2DATE and its foundations, which seeks to improve existing OTASU solutions by considering safety, security and availability from the ground up in an architecture that builds around composability and modularity. The research presented throughout this paper has received funding from the European Community’s Horizon 2020 programme under the UP2DATE project (grant agreement 871465).

Fairness in Secure Multiparty Computation (MPC) is known to be impossible to achieve in the presence of a dishonest majority. Previous works have proposed combining MPC protocols with cryptocurrencies in order to financially punish aborting adversaries, providing an incentive for parties to honestly follow the protocol. The focus of existing work is on proving that this approach is possible and unfortunately they present monolithic and mostly inefficient constructions. In this work, we put forth the first UC secure modular construction of “Insured MPC”, where either the output of the private computation (which describes how to distribute funds) is fairly delivered or a proof that a set of parties has misbehaved is produced, allowing for financial punishments. Moreover, both the output and the proof of cheating are publicly verifiable, allowing third parties to independently validate an execution. We present an efficient compiler that implements Insured MPC from an MPC protocol with certain properties, a standard (non-private) Smart Contract and a publicly verifiable homomorphic commitment scheme. As an intermediate step, we propose the first construction of a publicly verifiable homomorphic commitment scheme with composability guarantees.

The concept of Industry 4.0, or the Fourth Industrial Revolution, has the potential for radically increased system reconfigurability and flexibility. At its core, the notion of cyber-physical system, as the new generation of embedded systems with advanced artificial intelligence and improved communication capabilities, is seen as the key enabling concept that will render production activities more sustainable. The cyber-physical conceptualization dramatically reduces the integration effort by virtually eliminating the need, time, and cost for reprogramming. However, there are still important challenges that need to be addressed before one can start to design cyber-physical production systems consistently. These intertwine and are not as easily solvable as the popular science descriptions may suggest. This paper brings them forward and develops a critical comparative analysis between today’s automation solutions and their potential cyber-physical counterparts. The analysis considers the technical and conceptual challenges that are included in the process of migrating today’s, mostly bespoke, automation solutions to highly modularized, dynamic, and interactive cyber-physical production systems (CPPSs). In this context, this paper considers the interplay between form and function of industrial components, at the light of their cyber-physical formulation. At the same time, it addresses the system-level (de)composability and interaction design challenges that arise from the integration of modular CPPSs.

ALMARVI is a collaborative European research project funded by Artemis involving 16 industrial as well as academic partners across 4 countries, working together to address various computational challenges in image and video processing in 3 application domains: healthcare, surveillance and mobile. This paper is an editorial for a special issue discussing the integrated system created by the partners to serve as a cross-domain solution for the project. The paper also introduces the partner articles published in this special issue to discuss the various technological developments achieved within ALMARVI spanning all system layers, from hardware to applications. We illustrate the challenges faced within the project based on use cases from the three targeted application domains, and how these can address the 4 main project objectives addressing 4 challenges faced by high performance image and video processing systems: massive data rate, low power consumption, composability and robustness. We present a system stack composed of algorithms, design frameworks and platforms as a solution to these challenges. Finally, the use cases from the three different application domains are mapped on the system stack solution and are evaluated based on their performance for each of the 4 ALMARVI objectives.

Interface theories allow system designers to reason about the composability and compatibility of concurrent system components. Such theories often extend both de Alfaro and Henzinger’s Interface Automata and Larsen’s Modal Transition Systems, which leads, however, to several issues that are undesirable in practice: an unintuitive treatment of specified unwanted behaviour, a binary compatibility concept that does not scale to multi-component assemblies, and compatibility guarantees that are insufficient for software product lines. In this article we show that communication mismatches are central to all these problems and, thus, the ability to represent such errors semantically is an important feature of an interface theory. Accordingly, we present the error-aware interface theory EMIA, where the above shortcomings are remedied by introducing explicit fatal error states. In addition, we prove via a Galois insertion that EMIA is a conservative generalisation of the established Modal Interface Automata theory.

When analyzing the round complexity of multi-party protocols, one often overlooks the fact that underlying resources, such as a broadcast channel, can by themselves be expensive to implement. For example, it is well known that it is impossible to implement a broadcast channel by a (deterministic) protocol in a sublinear (in the number of corrupted parties) number of rounds. The seminal works of Rabin and Ben-Or from the early 1980s demonstrated that limitations as the above can be overcome by using randomization and allowing parties to terminate at different rounds, igniting the study of protocols over point-to-point channels with probabilistic termination and expected constant round complexity. However, absent a rigorous simulation-based definition, the suggested protocols are proven secure in a property-based manner or via ad hoc simulation-based frameworks, therefore guaranteeing limited, if any, composability. In this work, we put forth the first simulation-based treatment of multi-party cryptographic protocols with probabilistic termination. We define secure multi-party computation (MPC) with probabilistic termination in the UC framework and prove a universal composition theorem for probabilistic termination protocols. Our theorem allows to compile a protocol using deterministic termination hybrids into a protocol that uses expected constant round protocols for emulating these hybrids, preserving the expected round complexity of the calling protocol. We showcase our definitions and compiler by providing the first composable protocols (with simulation-based security proofs) for the following primitives, relying on point-to-point channels: (1) expected constant round perfect Byzantine agreement, (2) expected constant round perfect parallel broadcast, and (3) perfectly secure MPC with round complexity independent of the number of parties.

Current simulation models are not only represented in the form of traditional data or formula for pure theory analysis but expanded to be simulation modeling assets that are featured with complicated structure, diverse behaviors, and abundant semantics. Semantic composability, therefore, receives a constantly growing attention in recent years. So far, one of the popular solutions to enhance semantic expressiveness is domain specific modeling based on general metamodeling (GMM) facilities. But for some particular domains, researchers identified the need of deeper semantic expressiveness therefore proposed domain specific metamodeling (DSMM). Hence, this paper aims to explore the underlying methodologies of DSMM for supporting deep semantic composability. Compared with several usual alternatives based on GMM, this paper applies the multi-level metamodeling architecture to create a set of metamodeling primitives using an example named SEvent. In fact, SEvent is a novel formalism that slightly extends Petri net to support continuous states transition and continuous event triggering. As a proof of concept, we concentrate on developing the textual syntax of SEvent and using it to represent torpedo’s behaviors.

Over-The-Air Software Updates (OTASU) are gaining popularity on the safety-critical domain. The motivation behind this trend is twofold. On the one hand, the ability of adding new functionality and services to the system without a complete redesign makes product makers more competitive and improves user experience. On the other hand, the increasing connectivity of emerging embedded devices makes OTASU a crucial cyber-security demand to keep the system up-to-date with latest security patches. However, the application of OTASU in the safety-critical domain is not straightforward, as they are not contemplated by current functional safety standards. The UP2DATE European H2020 project, seeks to provide solutions to cope with the challenging requirements of safety and security standards with respect to software updates. This paper gives an overview of UP2DATE, its foundations and the initial description of its safe and secure architecture that builds around composability and modularity on heterogeneous high-performance platforms.

Many solutions for composability and compositionality rely on specifying the interface for a component using bandwidth. Some previous works specify period (P) and budget (Q) as an interface for a component. Q/P provides us with a bandwidth (the share of a processor that this component may request); P specifies the time granularity of the allocation of this processing capacity. Other works add another parameter, deadline , which can help to provide tighter bounds on how this processing capacity is distributed. Yet other works use the parameters α and Δ where α is the bandwidth and Δ specifies how smoothly this bandwidth is distributed. It is known [4] that such bandwidth-like interfaces carry a cost: there are tasksets that could be guaranteed to be schedulable if tasks were scheduled directly on the processor, but with bandwidth-like interfaces, it is impossible to guarantee the taskset to be schedulable. It is known that this penalty can be infinite, i.e., the use of bandwidth-like interfaces may require the use of a processor that has a speed that is k times faster, and one can show this for any k. This brings the following question: "What is the average-case performance penalty of bandwidth-like interfaces?" A previous paper [5] has partially answered this question by stating an expression on this penalty as a function of taskset parameters and then randomly generated tasksets to obtain a probability distribution of this penalty. In this paper, we answer this question analytically for the case that the taskset has tasks with infinite minimum inter-arrival time, equal task density, uniformly distributed deadlines, and the number of tasks approaches infinity. For this specific case, we derive an expression; if deadlines are uniformly distributed in [0,1], then we find that the penalty is two. We also run experiments to explore systems with these assumptions but for finite number of tasks. From these experiments, we conclude that (i) the larger the number of tasks is, the larger the penalty is, (ii) the larger the number of tasks is, the less skewed the probability distribution is, and (iii) the larger the number of tasks is, the smaller the variance of the penalty is. We are currently working on the case where deadlines follow other distributions.

The emergence of cloud computing infrastructure and Semantic Web technologies has created unprecedented opportunities for composing large-scale business processes and workflow-based applications that span multiple organizational domains. A key challenge related to composition of such multi-organizational business processes and workflows is posed by the security and access control policies of the underlying organizational domains. In this paper, we propose a framework for verifying secure composability of distributed workflows in an autonomous multi-domain environment. The objective of workflow composability verification is to ensure that all the users or processes executing the designated workflow tasks conform to the time-dependent security policy specifications of all collaborating domains. A key aspect of such verification is to determine the time-dependent schedulability of distributed workflows, assumed to be invoked on a recurrent basis. We use a two-step approach for verifying secure workflow composability. In the first step, a distributed workflow is decomposed into domain-specific projected workflows and is verified for conformance with the respective domain's security and access control policy. In the second step, the cross-domain dependencies amongst the workflow tasks performed by different collaborating domains are verified.

Object composability simplifies reasoning of multi-object concurrent executions.Linearizable objects are composable, but have a high performance price.We define a new type of objects, OSC, that are performant and can be composed.OSC captures the typical non-linearizable behavior of real-world services.This paper formally proves OSC composability correctness. We define ordered sequential consistency (OSC), a generic criterion for concurrent objects. We show that OSC encompasses a range of criteria, from sequential consistency to linearizability, and captures the typical behavior of real-world coordination services, such as ZooKeeper. A straightforward composition of OSC objects is not necessarily OSC, e.g., a composition of sequentially consistent objects is not sequentially consistent. We define a global property we call leading ordered operations, and prove that it enables correct OSC composition.

This paper presents a conceptual model design for entity-level combat simulation to enhance model composability and reusability. For conceptual modeling, we first describe the following three probl...

The article of record as published may be found at https://doi.org/10.1016/j.procs.2017.09.005 Unmanned aerial vehicle (UAV) swarm design and mission application is a burgeoning area of research. The applications of swarm technology to unmanned systems are in the infancy of realization, although clear benefits from the enhanced capabilities can be easily envisioned for commercial and government missions including persistent search, long-term monitoring, sensor data collection, object retrieval, and offensive attack missions. Designing a UAV swarm architecture without considering the mission doctrine is imprudent, as is developing a mission doctrine without understanding the unique capabilities of swarm technology. This paper provides a literature review of current UAV swarm design architectures, most of which employ bottom-up methods with little consideration for an intended operational mission. The proposed top-down framework and modeling approach is focused on developing a modular UAV swarm playbook-type architecture by decomposing a subset of US Navy missions, and developing tactics that can be applied to those missions and support future missions. The framework proposed herein is derived from a consolidation of heuristics from the model-based systems engineering, robotics, human systems integration, biology, and computer science disciplines. The goal of this framework is to provide a standard methodology for designing and operating swarm unmanned systems that will meet the performance requirements for military missions. In this paper, an integrated framework for designing a UAV swarm system with swarm mission doctrine as a primary design factor is presented.

We show that random oblivious transfer protocols that are statistically secure according to a definition based on a list of information-theoretical properties are also statistically universally composable. That is, they are simulatable secure with an unlimited adversary, an unlimited simulator, and an unlimited environment machine. Our result implies that several previous oblivious transfer protocols in the literature that were proven secure under weaker, non-composable definitions of security can actually be used in arbitrary statistically secure applications without lowering the security.

Composability is one of seven reasons for the long-standing and continuing success of MPI. Extending MPI by composing its operations with user-level operations provides useful integration with the progress engine and completion notification methods of MPI. However, the existing extensibility mechanism in MPI (generalized requests) is not widely utilized and has significant drawbacks. MPI can be generalized via scheduled communication primitives, for example, by utilizing implementation techniques from existing MPI-3 nonblocking collectives and from forthcoming MPI-4 persistent and partitioned APIs. Non-trivial schedules are used internally in some MPI libraries; but, they are not accessible to end-users. Message-based communication patterns can be built as libraries on top of MPI. Such libraries can have comparable implementation maturity and potentially higher performance than MPI library code, but do not require intimate knowledge of the MPI implementation. Libraries can provide performance-portable interfaces that cross MPI implementation boundaries. The ability to compose additional user-defined operations using the same progress engine benefits all kinds of general purpose HPC libraries. We propose a definition for MPI schedules: a user-level programming model suitable for creating persistent collective communication composed with new application-specific sequences of user-defined operations managed by MPI and fully integrated with MPI progress and completion notification. The API proposed offers a path to standardization for extensible communication schedules involving user-defined operations. Our approach has the potential to introduce event-driven programming into MPI (beyond the tools interface), although connecting schedules with events comprises future work. Early performance results described here are promising and indicate strong overlap potential.

Modern embedded many-core systems host, among others, real-time applications which must be dynamically launched at run time. To this end, Hybrid Application Mapping (HAM) methodologies combine design-time analysis with runtime mapping techniques to enable dynamic application mapping with performance guarantees, e.g., w.r.t. real-time constraints. They rely on composability to derive the required performance guarantees in an isolated analysis of individual applications at design time. The ongoing process technology downsizing, however, has given rise to an increased on-chip temperature, so that the thermal integrity of the platform must be monitored and enforced at run time by means of Dynamic Thermal Management (DTM) techniques which use countermeasures e.g. DVFS and power gating. This, however, violates composability, as the thermally unsafe behavior of one application may trigger DTM countermeasures that affect other applications running in the thermally affected region which, in turn, may lead to the violation of their real-time constraints. As a remedy, this paper proposes, for the first time, a thermally composable HAM methodology that enforces thermal safety proactively at the launch time of applications and, thereby, prevents DTM interferences which react to thermal violations. To that end, we present (a) a novel thermal-safety analysis that can be integrated into the design-time analysis of HAM and (b) a set of thermal-safety admission checks that can be used at run time when launching an application. By establishing thermal composability among running applications, the proposed HAM approach enables providing thermally safe real-time guarantees for dynamically mapped applications in many-core systems. Experimental results for a variety of hard real-time applications on multiple heterogeneous many-core architectures demonstrate the efficiency and effectiveness of the proposed methodology.

Over the last few years, we have experienced a massive transformation of the Software Defined Networking ecosystem with the development of SDNEnhancements, e.g., Statesman, ESPRES, Pane, and Pyretic, to provide better composability, better utilization of TCAM, consistent network updates, or congestion free updates. The end-result of this organic evolution is a disconnect between the SDN applications and the data-plane. A disconnect which can impact an SDN application's performance and efficacy. In this paper, we present the first systematic study of the interactions between SDNEnhancements and SDN applications -- we show that an SDN application's performance can be significantly impacted by these SDNEnhancements: for example, we observed that the efficiency of a traffic engineering SDN application was reduced by 24.8%. Motivated by these insights, we present, Mozart, a redesigned SDN controller centered around mitigating and reducing the impact of these SDNEnhancements. Using two prototypes interoperating with seven SDN applications and two SDNEnhancements, we demonstrate that our abstractions require minimal changes and can restore an SDN application's performance. We analyzed Mozart's scalability and overhead using large scale simulations of modern cloud networks and observed them to be negligible.

Future intelligent management systems compose of heterogeneous devices with lots of applications cooperatively to analyze data. Recent advances in Service-Oriented Architecture (SOA) provide opportunities for solving this challenge with reusability, composability, and abstraction. In this paper, we develop an intelligent management platform using cloud computing and SOA. It provides different service modules to collect, store, analyze, and visualize the Internet of Things (IoT) data to fit Cyber-Physical System (CPS), all these functions are wrapped as RESTful APIs so it can be imported to other applications, making the same service can be reused to meet the requirements of different fields. In addition, an editable dynamic dashboard service is proposed to personalize the management demand in a click-and-drag way. Users can analyze and observe data for intelligent management. Finally, we apply these services to build three different applications to validate the power of our service platform.

Programmers, researchers, system administrators, and data scientists often build complex workflows based on command-line applications. To give these power users the well-known benefits of GUIs, we created Bespoke, a system that synthesizes custom GUIs by observing user demonstrations of command-line apps. Bespoke unifies the two main forms of desktop human-computer interaction (command-line and GUI) via a hybrid approach that combines the flexibility and composability of the command line with the usability and discoverability of GUIs. To assess the versatility of Bespoke, we ran an open-ended study where participants used it to create their own GUIs in domains that personally motivated them. They made a diverse set of GUIs for use cases such as cloud computing management, machine learning prototyping, lecture video transcription, integrated circuit design, remote code deployment, and gaming server management. Participants reported that the benefit of these bespoke GUIs was that they exposed only the most relevant subset of options required for their specific needs. In contrast, vendor-made GUIs usually include far more panes, menus, and settings since they must accommodate a wider range of use cases.

With the rise of ever-more sophisticated wearables and sensing technologies, mobile health continues to be an active area of research. However, from a clinical researcher point of view, testing novel use of the mobile health innovations remains a major hurdle, as composing a clinical trial using a combination of technologies still remains in the realm of computer scientists. We take a software-inspired viewpoint of clinical trial designs to design, develop and validate HealthSense to enable expressibility of complex ideas, composability with diverse devices and services while maximally maintaining simplicity for a clinical research user. A key innovation in HealthSense is the concept of a study state manager (SSM) that modifies parameters of the study over time as data accumulates and can trigger external events that affect the participant; this design allows us to implement nearly arbitrary clinical trial designs. The SSM can funnel data streams to custom or third-party cloud processing pipelines and the result can be used to give interventions and modify parameters of the study. HealthSense supports both Android and iOS platforms and is secure, scalable and fully operational. We outline three trials (two with clinical populations) to highlight simplicity, composability, and expressibility of HealthSense.

Power and electromagnetic based side-channel attacks are serious threats against the security of cryptographic embedded devices. In order to mitigate these attacks, implementations use countermeasures, among which masking is currently the most investigated and deployed choice. Unfortunately, commonly studied forms of masking rely on underlying assumptions that are difficult to satisfy in practice. This is due to physical defaults, such as glitches or transitions, which can recombine the masked data in a way that concretely reduces an implementation’s security.

This paper presents Polycube, an open-source softwareframework based on eBPF, that enables the creation ofarbitrary and complex network function chains. Each functioncan include an efficient in-kernel data plane and a flexible userspacecontrol plane with strong characteristics of isolation, persistence(e.g., across server reboots) and composability. In addition,a generic model for the control and management plane of eachnetwork function simplifies the manageability and accelerates thedevelopment of new network services. We validate the frameworkby creating different network services and benchmarking theirperformance in a complex scenario, namely a network providerfor Kubernetes. Results show that Polycube programs are about20x shorter than equivalent programs implemented with vanilla eBPF.

Composability is a key component to improve programmers’ productivity in writing fast market-expanding applications such as parallel machine learning algorithms and big data analytics. These applications exhibit both regular and irregular compute patterns, and are often combined with other functions or libraries to compose a larger program. However, composable parallel processing has taken a back seat in many existing parallel programming libraries, making it difficult to achieve modularity in large-scale parallel programs. In this paper, we introduce a new parallel task programming library using composable tasking graphs. Our library efficiently supports task parallelism together with an intuitive task graph construction and flexible execution API set to enable reusable and composable task dependency graphs. Developers can quickly compose a large parallel program from small and modular parallel building blocks, and easily deploy the program on a multicore machine. We have evaluated our library on real-world applications. Experimental results showed our library can achieve comparable performance to Intel Threading Building Blocks with less coding effort.

Hybrid practices are emerging that integrate creative materials like paint, clay, and cloth with intangible immaterials like computation, electricity, and heat. This work aims to expand the design potential of immaterial elements by transforming them into manipulatable, observable and intuitive materials. We explore one such immaterial, electric heat, and develop a maker-friendly fabrication pipeline and crafting support tool that allows users to experientially compose resistive heaters that generate heat spatially and temporally. These heaters are then used to couple heat and thermoreactive materials in a class of artifacts we term Thermoreactive Composites (TrCs). In a formal user study, we observe how designing fabrication workflows along dimensions of composability and perceivability better matches the working styles of material practitioners without domain knowledge of electronics. Through exemplar artifacts, we demonstrate the potential of heat as a creative material and discuss implications for immaterials used within creative practices.

Part 2: Short and “Journal First” Papers; International audience; Computational fields have been proposed as an effective abstraction to fill the gap between the macro-level of distributed systems (specifying a system’s collective behaviour) and the micro-level (individual devices’ actions of computation and interaction to implement that collective specification), thereby providing a basis to better facilitate the engineering of collective APIs and complex systems at higher levels of abstraction. This approach is particularly suited to complex large-scale distributed systems, like the Internet-of-Things and Cyber-Physical Systems, where new mechanisms are needed to address composability and reusability of collective adaptive behaviour. This work introduces a full formal foundation for field computations, in terms of a core calculus equipped with typing, denotational, and operational semantics. Critically, we apply techniques for formal programming languages to collective adaptive systems: we provide formal establishment of a link between the micro- and macro-levels of collective adaptive systems, via a result of computational adequacy and abstraction for the (aggregate) denotational semantics with respect to the (per-device) operational semantics.

Convolutional Neural Networks (CNN) are widely used for Deep Learning tasks. CNN pruning is an important method to adapt a large CNN model trained on general datasets to fit a more specialized task or a smaller device. The key challenge is on deciding which filters to remove in order to maximize the quality of the pruned networks while satisfying the constraints. It is time-consuming due to the enormous configuration space and the slowness of CNN training. The problem has drawn many efforts from the machine learning field, which try to reduce the set of network configurations to explore. This work tackles the problem distinctively from a programming systems perspective, trying to speed up the evaluations of the remaining configurations through computation reuse via a compiler-based framework. We empirically uncover the existence of composability in the training of a collection of pruned CNN models, and point out the opportunities for computation reuse. We then propose composability-based CNN pruning, and design a compression-based algorithm to efficiently identify the set of CNN layers to pre-train for maximizing their reuse benefits in CNN pruning. We further develop a compiler-based framework named Wootz, which, for an arbitrary CNN, automatically generates code that builds a Teacher-Student scheme to materialize composability-based pruning. Experiments show that network pruning enabled by Wootz shortens the state-of-art pruning process by up to 186X while producing significantly better pruning results.