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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.

A fundamental challenge in synthetic biology is to create molecular circuits that can program complex cellular functions. Because proteins can bind, cleave, and chemically modify one another, and interface directly and rapidly with endogenous pathways, they could extend the capabilities of synthetic circuits beyond what is possible with gene regulation alone. However, the very diversity which makes proteins so powerful also complicates efforts to harness them as well-controlled synthetic circuit components. Recent work has begun to address this challenge, focusing on principles such as orthogonality and composability that permit construction of diverse circuit-level functions from a limited set of engineered protein components. These approaches are now enabling the engineering of circuits that can sense, transmit, and process information, dynamically control cellular behaviors, and enable new therapeutic strategies, establishing a powerful paradigm for programming biology.

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.

The human immune system is studied at different scales (molecular, cellular, tissue, organism, population) with varied granularity and spatiotemporal dimensions. Innate and adaptive immune responses to viral infections are researched in vitro, in vivo, and in silico using mathematical and computational methods. The construction of computational models is an iterative and interactive process which combines existing knowledge and hypotheses (knowledge-driven approach) with empirical datasets (data-driven approach). The resulting models should ideally provide reusability, composability, and interoperability to support system simulation. The main premises of this paper are (1) computational models can be used not only for representation of existing knowledge, but also as experimental tools to demonstrate, exemplify, create, and serve as test-beds for the analysis of empirical data;and (2) computational models should be evaluated in terms of their usability (defined as a measure of how well a specified group of users can achieve predefined goals effectively, efficiently, and satisfactorily in a specified context of use). The usability measure is specified by the context information: user characteristics, their goals, and additional constraints (e.g., data availability, temporal limitations). This paper focuses on the modelling process performed by multidisciplinary teams and the usability of models for the exploration of immune response to viral infections. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference, June 2021.

In this paper, we study Local Information Privacy (LIP). As a context-aware privacy notion, LIP relaxes the de facto standard privacy notion of local differential privacy (LDP) by incorporating prior knowledge and therefore achieving better utility. We study the relationships between LIP and some of the representative privacy notions including LDP, mutual information and maximal leakage. We show that LIP provides strong instance-wise privacy protection compared to other context-aware privacy notions. Moreover, we present some useful properties of LIP, including post-processing, linkage, composability, transferability and robustness to imperfect prior knowledge. Then we study a general utility-privacy tradeoff framework, under which we derive LIP based privacy-preserving mechanisms for both discrete and continuous-valued data. Three types of perturbation mechanisms are studied in this paper: 1) randomized response (RR), 2) random sampling (RS) and 3) additive noise (AN) (e.g., Gaussian mechanism). Our privacy mechanisms incorporate the prior knowledge into the perturbation parameters so as to enhance utility. Finally, we present a comprehensive set of experiments on real datasets to illustrate the advantage of context-awareness and compare the utility-privacy tradeoffs provided by different mechanisms.

Component-based design has received considerable attention owing to its advantages in terms of security and safety when developing modern embedded systems. To effectively allocate computing resources to components in these systems, real-time component-based scheduling theory has been studied from various perspectives. The main advantage of component-based scheduling theory is that it guarantees the schedulability of an independent component and composability of multiple components. However, the existing component scheduling theory cannot be directly applied to real hardware platform due to an impractical assumption that resource allocation must be conducted across the continuum of real numbers, whereas actual operating systems (or virtualization systems) allocate resources in units of scheduling time quantum. In this study, we proposed a new efficient resource allocation and supply mechanism for quantumized hardware platforms while using real-number-based component interface. In simulation results with randomly-generated workloads, our approach reduced the overhead of existing approaches by up to 97.1% in an individual component. In composition of multiple components, our approach has up to 0.41 better acceptance ratio than existing approaches.

The design of codes for communicating reliably over a statistically well defined channel is an important endeavor involving deep mathematical research and wide-ranging practical applications. In this work, we present the first family of codes obtained via deep learning, which significantly beats state-of-the-art codes designed over several decades of research. The communication channel under consideration is the Gaussian noise channel with feedback, whose study was initiated by Shannon; feedback is known theoretically to improve reliability of communication, but no practical codes that do so have ever been successfully constructed. We break this logjam by integrating information theoretic insights harmoniously with recurrent-neural-network based encoders and decoders to create novel codes that outperform known codes by 3 orders of magnitude in reliability. We also demonstrate several desirable properties of the codes: (a) generalization to larger block lengths, (b) composability with known codes, (c) adaptation to practical constraints. This result also has broader ramifications for coding theory: even when the channel has a clear mathematical model, deep learning methodologies, when combined with channel-specific information-theoretic insights, can potentially beat state-of-the-art codes constructed over decades of mathematical research., 24 pages, 20 figures

This paper presents a case study that demonstrates how tools from compositional verification can be used to design and analyze complex multi-agent systems operating in dynamic and uncertain environments. The case study concerns the design of an unmanned multi-aircraft system tasked to compromise an aerial encroacher by deploying countermeasures. The constituent agents, termed defenders, are fixed-wing unmanned aircraft. To successfully compromise the encroacher, at least one defender must be within a prespecified distance from the encroacher for a certain period, and the defenders must avoid collision among themselves and with the encroacher. Verifying this global property using monolithic (system-level) verification techniques is a challenging task due to the complexity of the components (defenders) and the interactions among them. To overcome these challenges, the components are designed to have a modular architecture, thereby enabling the use of component-based reasoning to simplify the task of verifying the global system property. Results from Euclidean geometry and formal methods are used to prove most component properties. For properties where analytical tools are overly conservative, focused Monte Carlo simulations are carried out. Restricting the use of simulations (or testing) to local verification of partial component properties leads to increasing the reliability of the system. National Science Foundation (NSF)National Science Foundation (NSF) [CMMI-1351640, CNS-1801611]; Center for Unmanned Aircraft Systems (C-UAS), an NSF sponsored industry/university cooperative research center (I/UCRC), under NSF [IIP-1539975, CNS-1650465]; C-UAS industry members This work was supported in part by the National Science Foundation (NSF) under Grant CMMI-1351640 and Grant CNS-1801611, and in part by the Center for Unmanned Aircraft Systems (C-UAS), an NSF sponsored industry/university cooperative research center (I/UCRC), under NSF Grant IIP-1539975 and Grant CNS-1650465, along with significant contributions from C-UAS industry members.

Posté sur BioRxiv le 4 août 2021; International audience; Mathematical modeling has become a major tool to guide the characterization and synthetic construction of cellular processes. However, models typically lose their capacity to explain or predict experimental outcomes as soon as any, even minor, modification of the studied system or its operating conditions is implemented. This limits our capacity to fully comprehend the functioning of natural biological processes and is a major roadblock for the de novo design of complex synthetic circuits. A common cause of this problem is that cell-to-cell variability creates couplings between single-cell circuits and population processes such as selection or growth. Altering the circuit may then have unforeseen consequences inside growing populations. Here we construct a yeast optogenetic differentiation system that exploits cell-to-cell variability to enable external control of the population composition. We show that a simple deterministic model can explain the dynamics of the core system. However, modifying the context of the circuit by expressing system components from plasmids leads to failure of model predictions. Subsequently, we deploy theory from stochastic chemical kinetics to construct models of the system’s components that simultaneously track single-cell and population processes and demonstrate that this allows us to quantitatively predict emerging dynamics of the plasmid-based system without any adjustment of model parameters. We conclude that carefully characterizing the dynamics of cell-to-cell variability using appropriate modeling theory may allow one to unravel the complex interplay of stochastic single-cell and population processes and to predict the functionality of complex synthetic circuits in growing populations before the circuit is constructed.

Current higher-order calculations heavily rely on publicly available tools. These are not necessarily computer programs, but it is anything that aids in different steps of calculations, e.g. also includes published loop-amplitudes, integrals and algorithms that eventually lead to important automatization. In these proceedings we give an overview of tools used for perturbative precision calculations. The availability, composability and hackability of such tools are what lets our field flourish. And while our field is already positively standing out in the development of such tools, we would like to stress their importance for the success of future developments.

In this paper, we propose ERGO (edge architecture for Ag-IoT), an edge-computing architecture for infrastructureless smart agriculture environments. We also develop Ag-IoT application APIs and the associated microservice infrastructure. Our implementation and evaluations show that ERGO can operate independently of cloud-backed assistance, is highly scalable, modular, and affords composability benefits to Ag-IoT systems. We also demonstrate that ERGO outperforms traditional infrastructure in response latencies and transactional throughput, on average, by over 54% and 77%, respectively.

Digital twin has the potential to be an important concept for achieving smart manufacturing. However, there remains a lot of confusion about the concept and how it can be implemented in real manufacturing systems, especially among small-to-medium-sized enterprises. This paper synthesizes the different perspectives that have been reported on the digital twin to identify the key characteristics that must be understood when developing a digital twin for a specific use case. Example applications are provided and the need for a standardized framework, such as the one under development as ISO 23247 (Digital Twin Manufacturing Framework), is motivated. This framework can enable context-dependent implementations and promote composability and reusability of digital twin components.

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.

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.

Working within the framework of Distributed Morphology (Halle and Marantz 1993, 1994), this paper offers a derivational analysis of the range of structures and the types of idiosyncrasy associated with compounding. Building on prior analysis by Harley (2009), compound structures are argued to vary according to the ways in which the head and the non-head of a compound are categorised. Specifically, if the non-head of a compound is acategorial, then the relationship between the compound head and non-head is non-decomposable. Based on data from Hebrew (Borer 2009), it is shown that this also makes the non-head inaccessible to independent syntactic-semantic operations, including coordination, and coreference with a pronoun. It is additionally shown that morphologically-conditioned allomorphy (Bobaljik 2012) may be conditioned between the compound head and a suffix, as constitutes part of a bracketing paradox (Williams 1981). Where categorisation of the head of the compound gives rise to effects of headedness, however, this allomorphy may be ‘blocked’ by the structure associated with exocentricity. The final sections of the paper consider exocentricity, and other interactions between idiosyncratic meanings and phonology, in further detail.

This perspectives article addresses risk in cyber defense and identifies opportunities to incorporate risk analysis principles into the cybersecurity field. The Science of Security (SoS) initiative at the National Security Agency seeks to further and promote interdisciplinary research in cybersecurity. SoS organizes its research into the Five Hard Problems (5HP): (1) scalability and composability; (2) policy-governed secure collaboration; (3) security-metrics-driven evaluation, design, development, and deployment; (4) resilient architectures; and (5) understanding and accounting for human behavior. However, a vast majority of the research sponsored by SoS does not consider risk and when it does so, only implicitly. Therefore, we identify opportunities for risk analysis in each hard problem and propose approaches to address these objectives. Such collaborations between risk and cybersecurity researchers will enable growth and insight in both fields, as risk analysts may apply existing methodology in a new realm, while the cybersecurity community benefits from accepted practices for describing, quantifying, working with, and mitigating risk.

Simulation models are widely used to study complex systems. Current simulation models are generally handcrafted using expert knowledge (knowledge-driven); however, this process is slow and introduces modeler bias. This article presents an approach towards data-driven simulation modeling by developing a framework that discovers simulation models in an automated way for mobile agent-based applications. The framework is comprised of three components: (1) a model space specification, (2) a search method (genetic algorithm), and (3) framework measurement metrics. The model space specification provides a formal specification for the general model structure from which various models can be generated. The search method is used to efficiently search the model space for candidate models that exhibit desired behavior patterns. The five framework measurement metrics: flexibility, comprehensibility, controllability, composability, and robustness, are developed to evaluate the overall framework. The results demonstrate that it is possible to discover a variety of interesting models using the framework.

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.

Microservices-based architectures gain more and more attention in industry and academia due to their tremendous advantages such as providing resiliency, scalability, composability, etc. To benefit from these advantages, a proper architectural design is very important. The decomposition model of services into microservices and the granularity of these microservices affect the different aspects of the system such as flexibility, maintainability, performance, and security. An inappropriate service decomposition into microservices (improper granularity) may increase the attack surface of the system and lower its security level. In this paper, first, we study the probability of compromising services before and after decomposition. Then we formulate the impacts of possible service decomposition models on confidentiality, integrity, and availability attributes of the system. To do so, we provide equations for measuring confidentiality, integrity, and availability risks of the decomposed services in the system. It is also shown that the number of entry points to the decomposed services and the size of the microservices affect the security attributes of the system. As a use case, we propose three different service decomposition models for the 5G NRF (Network Repository Function) and calculate the impacts of these decomposition models on the confidentiality, integrity, and availability of the system using the provided equations.

Host-driven execution of applications on modern GPU-accelerated systems suffer from frequent host-device synchronizations, data movement and limited flexibility in scheduling user tasks. We present CharminG, a runtime system designed to run entirely on the GPU without any interaction with the host. CharminG takes inspiration from the Charm++ parallel programming system and implements processor virtualization and message-driven execution on the GPU. We evaluate the composability and preliminary performance of CharminG with a proxy application that performs the Jacobi iterative method in a two-dimensional grid, using the Lassen supercomputer at Lawrence Livermore National Laboratory.

Background Delivering tools for genome analysis to users is often difficult given the complex dependencies and conflicts of such tools. Container virtualization systems (such as Singularity) isolate environments, thereby helping developers package tools. However, these systems lack mutual composability, i.e., an easy way to integrate multiple tools in different containers and/or on the host. Another issue is that one may be unable to use a single container system of the same version at all the sites being used, thus discouraging the use of container systems. Results We developed LPMX, an open-source pure rootless composable container system that provides composability; i.e., the system allows users to easily integrate tools from different containers or even from the host. LPMX accelerates science by letting researchers compose existing containers and containerize tools/pipelines that are difficult to package/containerize using Conda or Singularity, thereby saving researchers’ precious time. The technique used in LPMX allows LPMX to run purely in userspace without root privileges even during installation, thus ensuring that we can use LPMX at any Linux clusters with major distributions. The lowest overhead for launching containers with LPMX gives us courage to isolate tools as much as possible into small containers, thereby minimizing the chance of conflicts. The support for the layered file system keeps the total size of container images for a single genomic pipeline modest, as opposed to Singularity, which uses mostly a flat single-layer image. Conclusions LPMX is pure rootless container engine with mutual composability, thus saving researchers’ time, and accelerating science.

Proofs in simulation-based frameworks have the greatest rigor when they are machine checked. But the level of details in these proofs surpasses what the formal-methods community can handle with existing tools. Existing formal results consider streamlined versions of simulation-based frameworks to cope with this complexity. Hence, a central question is how to abstract details from composability results and enable their formal verification.In this paper, we focus on the modeling of system communication in composable security statements. Existing formal models consider fixed communication patterns to reduce the complexity of their proofs. However, as we will show, this can affect the reusability of security statements. We propose an abstract approach to modeling system communication in Constructive Cryptography that avoids this problem. Our approach is suitable for mechanized verification and we use CryptHOL, a framework for developing mechanized cryptography proofs, to implement it in the Isabelle/HOL theorem prover. As a case study, we formalize the construction of a secure channel using Diffie-Hellman key exchange and a one-time-pad.

The concepts of information flow security and refinement are known to have had a troubled relationship ever since the seminal work of McLean. In this work we study refinements that support changes in data representation and semantics, including the addition of state variables that may induce new observational power or side channels. We propose a new epistemic approach to ignorance-preserving refinement where an abstract model is used as a specification of a system’s permitted information flows, that may include the declassification of secret information. The core idea is to require that refinement steps must not induce observer knowledge that is not already available in the abstract model. Our study is set in the context of a class of shared variable multiagent models similar to interpreted systems in epistemic logic. We demonstrate the expressiveness of our framework through a series of small examples and compare our approach to existing, stricter notions of information-flow secure refinement based on bisimulations and noninterference preservation. Interestingly, noninterference preservation is not supported “out of the box” in our setting, because refinement steps may introduce new secrets that are independent of secrets already present at abstract level. To support verification, we first introduce a “cube-shaped” unwinding condition related to conditions recently studied in the context of value-dependent noninterference, kernel verification, and secure compilation. A fundamental problem with ignorance-preserving refinement, caused by the support for general data and observation refinement, is that sequential composability is lost. We propose a solution based on relational pre-and postconditions and illustrate its use together with unwinding on the oblivious RAM construction of Chung and Pass.

We introduce notions of Galois biconnections, intended to be the bilattice analogue of classical Galois connections between lattices. A bidirectional Galois biconnection is a (compatible) pair of Galois connections, the first relating truth orderings and the second relating knowledge orderings, while a unidirectional Galois biconnection is a Galois connection equipped with extra properties that seek to capture the bilattice structure. A further distinction is made between strong Galois biconnections which furnish bilattice-isomorphic images and regular Galois biconnections which induce order-isomorphic images of the maps. We investigate all four species of Galois biconnections on pre-bilattices and on bilattices with negation and conflation. We examine both the survival of elegant properties of Galois connections (composability, invertibility, preservation of joins and meets, etc.) and the preservation of interesting bilattice properties (distributivity, boundedness, interlacing) for the images of the bilattices under the Galois biconnection. Finally, we discuss the naturally emerging biclosure operators on bilattices.

Cyberinfrastructure is everywhere in diverse forms in service of applications in science, business and society. From IoT to extreme-scale computing data and computing have never been so distributed with the potential for real-time integration into these applications. The common theme to these applications, mostly composed of (big) data-integrated workloads, is their need to run in specialized environments for reasons such as on-demand or 24x7 nature of the tasks they are performing, and difficulties regarding their portability, latency, privacy, and performance optimization. Moreover, in many data-driven scientific applications, there is a need for heterogeneous integration of tasks requiring specialized computing capabilities with traditional high-throughput computing or high-performance computing tasks. Although some key middleware technologies enabled demonstration of standalone heterogeneous applications, such integration requires expertise convergence from a large group of people in very specialized settings. There are still many challenges for streamlined, scalable, repeatable, responsible, and explainable integration of data-integrated applications. Key opportunities for further innovations include intelligent systems and automated workflow management software that can compose and steer dynamic applications that can adapt to changing conditions in a data-driven fashion while integrating many tools to explore, analyze and utilize data. This talk will discuss some examples for data-integrated applications, describe emerging systems that enabled these applications, and overview our recent research to enable composable applications including a convergence application development methodology, intelligent middleware, and workflow composition.

Modern cloud computing workloads are becoming day by day more demanding, in terms of computational resources, as they feature multiple complex components, utilize heterogeneous hardware, and require tremendous amounts of memory. Such attributes of the emerging workloads disrupt the traditional design of cloud infrastructures, which are bound to decisions at the design time of the infrastructure, and mandate the dynamic composability of the next-generation cloud infrastructures.Scaling beyond the physical boundaries of the server trays, and minimizing over-provisioning of the nodes by disaggregating computational resources, such as CPUs, memory, etc., is a timely research problem. However, the prior line of research focuses on either NVM technologies or other disk-related approaches that try to relax the resource pressure induced by over-provisioning.In this work, we design, implement, and evaluate all necessary changes in the system software stack to support the dynamic allocation of disaggregated memory, depending on the needs of the workloads at hand. As a result, we can transparently increase the available memory of a system and achieve on average 57% better performance, compared to Swap, the de-facto mechanism used today for over-provisioning. Furthermore, we present a memory balancing policy that autonomously migrates memory pages across local and disaggregated memory. Our experimental results show that, even with a limited amount of local memory, memory balancing improves the performance by 41% on average, compared with using only disaggregated memory.

Detailed maps of the molecular basis of the disease are powerful tools for interpreting data and building predictive models. Modularity and composability are considered necessary network features for large-scale collaborative efforts to build comprehensive molecular descriptions of disease mechanisms. An effective way to create and manage large systems is to compose multiple subsystems. Composable network components could effectively harness the contributions of many individuals and enable teams to seamlessly assemble many individual components into comprehensive maps. We examine manually built versions of the RAS–RAF–MEK–ERK cascade from the Atlas of Cancer Signalling Network, PANTHER and Reactome databases and review them in terms of their reusability and composability for assembling new disease models. We identify design principles for managing complex systems that could make it easier for investigators to share and reuse network components. We demonstrate the main challenges including incompatible levels of detail and ambiguous representation of complexes and highlight the need to address these challenges.

Robotic testbeds used for flight and research technology development are highly complicated electromechanical systems demanding large engineering effort to configure and manage safety-critical hardware. The software that interoperates with robotic testbeds needs to be robust yet flexible to accommodate rapidly changing testbed needs and other operational demands in a cost-efficient manner. The Jet Propulsion Laboratory (JPL) presents a new open-source C++ library called Fastcat for powerful and flexible EtherCAT device configuration for testbed and robotic applications. Fastcat implements a set of C drivers called ‘Just SOEM Drivers’ built on the popular Simple Open EtherCAT Master library for a small, but highly tested set of supported EtherCAT devices. Fastcat offers a rich set of capabilities such as supporting an arbitrary number of EtherCAT Masters, a YAML input configuration file, and a visualization tool ‘Fcviz’ to graph the bus topology using the open-source Graphiz library. Furthermore, a novel feature called ‘Fastcat Signals’ can efficiently route data and commands between devices to compose self-contained closed loop control systems such as heaters or joint-torque controllers with zero added latency. Abstract device types like signal generators, functions, and PID controllers and offline versions of device hardware drivers allow for powerful composability, offline-only testing, and flexible hybrid partial online – offline bus topologies. The Fastcat core library is middleware agnostic, but ROS2 wrappers are developed for ease-of-use. This paper will describe design tradeoffs, an overview of the implementation of these capabilities, and early results from the first JPL Robotic testbed use.

Proving the security of masked implementations in theoretical models that are relevant to practice and match the best known attacks of the side-channel literature is a notoriously hard problem. The random probing model is a promising candidate to contribute to this challenge, due to its ability to capture the continuous nature of physical leakage (contrary to the threshold probing model), while also being convenient to manipulate in proofs and to automate with verification tools. Yet, despite recent progress in the design of masked circuits with good asymptotic security guarantees in this model, existing results still fall short when it comes to analyze the security of concretely useful circuits under realistic noise levels and with low number of shares. In this paper, we contribute to this issue by introducing a new composability notion, the Probe Distribution Table (PDT), and a new tool (called STRAPS, for the Sampled Testing of the RAndom Probing Security). Their combination allows us to significantly improve the tightness of existing analyses in the most practical (low noise, low number of shares) region of the design space. We illustrate these improvements by quantifying the random probing security of an AES S-box circuit, masked with the popular multiplication gadget of Ishai, Sahai and Wagner from Crypto 2003, with up to six shares.

Since the inception of Bitcoin, a plethora of distributed ledgers differing in design and purpose has been created. While by design, blockchains provide no means to securely communicate with external systems, numerous attempts towards trustless cross-chain communication have been proposed over the years. Today, cross-chain communication (CCC) plays a fundamental role in cryptocurrency exchanges, scalability efforts via sharding, extension of existing systems through sidechains, and bootstrapping of new blockchains. Unfortunately, existing proposals are designed ad-hoc for specific use-cases, making it hard to gain confidence in their correctness and composability. We provide the first systematic exposition of cross-chain communication protocols.

International audience; We present a composably secure protocol allowing $n$ parties to test an entanglement generation resource controlled by a possibly dishonest party. The test consists only in local quantum operations and authenticated classical communication once a state is shared among them and provides composable security, namely it can be used as a secure subroutine by $n$ honest parties within larger communication protocols to test if a source is sharing quantum states that are at least $\epsilon$-close to the GHZ state. This claim comes on top of previous results on multipartite entanglement verification where the security was studied in the usual game-based model. Here, we improve the protocol to make it more suitable for practical use in a quantum network and we study its security in the Abstract Cryptography framework to highlight composability issues and avoid hidden assumptions. This framework is a top-to-bottom theory that makes explicit any piece of information that each component (party or resource) gets at every time-step of the protocol. Moreover any security proof, which amounts to showing indistinguishability between an ideal resource having the desired security properties (up to local simulation) and the concrete resource representing the protocol, is composable for free in this setting. This allows us to readily compose our basic protocol in order to create a composably secure multi-round protocol enabling honest parties to obtain a state close to a GHZ state or an abort signal, even in the presence of a noisy or malicious source. Our protocol can typically be used as a subroutine in a Quantum Internet, to securely share a GHZ state among the network before performing a communication or computation protocol.

The term decentralized finance (DeFi) refers to an alternative financial infrastructure built on top of the Ethereum blockchain. DeFi uses smart contracts to create protocols that replicate existing financial services in a more open, interoperable, and transparent way. This paper highlights opportunities and potential risks of the DeFi ecosystem. I propose a multi-layered framework to analyze the implicit architecture and the various DeFi building blocks, including token standards, decentralized exchanges, decentralized debt markets, blockchain derivatives, and on-chain asset management protocols. I conclude that DeFi still is a niche market with certain risks but that it also has interesting properties in terms of efficiency, transparency, accessibility, and composability. As such, DeFi may potentially contribute to a more robust and transparent financial infrastructure. (JEL G15, G23, E59)

Decentralized financial (DeFi) applications on the Ethereum blockchain are highly interoperable because they share a single state in a deterministic computational environment. Stakeholders can deposit claims on assets, referred to as ‘liquidity shares’, across applications producing effects equivalent to rehypothecation in traditional financial systems. We seek to understand the degree to which this practice may contribute to financial integration on Ethereum by examining transactions in ‘composed’ derivatives for the assets DAI, USDC, USDT, ETH and tokenized BTC for the full set of 344.8 million Ethereum transactions computed in 2020. We identify a salient trend for ‘composing’ assets in multiple sequential generations of derivatives and comment on potential systemic implications for the Ethereum network.

Combat system modeling generally includes two aspects: structure and behavior. Structural modeling is to build the structure of entities and their internal and external static relationships. Behavioral modeling aims at the typical working processes. The simulation modeling definition language (SMDL) of Simulation Modeling Platform (SMP2) can describe system structure well, but it is difficult to support behavioral modeling. The modeling and simulation tool named Ptolemy is able to support the description of system behavior, but it lacks effective support for structural modeling. For this reason, the goal of the paper is to explore a mechanism to combine structural and behavioral modeling, which can not only effectively support the architecture design but also support the expression of system behaviors. As a proof of concept, we extend the SMDL by adding the synchronous data flow (SDF) elements, and use this new language, namely, the extended Simulation Model Definition Language (ESMDL), to specify a radar system for both of structural and behavioral modeling.

One of the benefits of decentralized finance (DeFi) – an alternative financial system built on blockchain – is composability, which means the system’s building blocks (tokens) can freely interact with one another to form new services. One example is stablecoin, a token with fixed exchange rate, which is backed by token collaterals. While stablecoins can be used to facilitate payments and exchanges, in DeFi they can be used to earn returns (“yield farming”), potentially multiplicatively. We use transaction-level blockchain data to analyze a stablecoin’s flows between protocols and provide suggestive evidence of DeFi yield-chasing behavior. We shed light on what DeFi total value locked might really measure and highlight the complexity in DeFi analysis and market surveillance.

A significant aspect of software integrity is the ability to determine whether a system is correctly showing the information it is intended to show. This work combines abstract cryptographic composability principles with automated machine learning techniques to support a new methodology for software integrity checking, particularly in the intractable domain of interface validation. A subtle but often perilously overlooked cryptographic principle is the requirement that a sender be aware of the meaning of inputs, specifically of the cleartext behind encrypted messages. We propose using computer vision to evaluate whether interfaces exhibit technical awareness of information content, enabling automated development-time and real-time integrity checking that is far more efficient and extensive than manual analysis.

A simulation System of Systems (SoS) comprised of various simulation systems may have dissimilar modeling paradigms. For performing autonomy research and development, various types of simulation systems need to be brought together to build a multi-domain virtual SoS wherein effects of autonomous entities/agents can be observed. While the distributed simulation community has solved the integrability challenge using standards like Distributed Interactive Simulation (DIS) or High Level Architecture (HLA), the model composability challenge is an open research problem. Many software/systems can now be made available as docker applications which can be readily plugged into existing simulation systems. However, the trust issues with such integration limit their usage as established systems cannot trust third party apps. This paper highlights some of the challenges with building a cloud-based simulation SoS, proposes an architecture framework using the concept of structural autonomy and leverages Modeling & Simulation as a fundamental key enabler for autonomy research.

To implement Service Oriented Architecture (SOA) in a banking company for transactional banking activity, this paper aims to overcome the problem of several related surrounding systems which has the same function in the different system by using Domain Driven Design. The paper proposes an approach to SOA using Web Services for service delivery in the area of transactional banking activities. Several systems will be connected to a BUS that will wait on and manage any request coming from other applications. To design the system then in this paper also will identify which application will consume the same function and analyze which function belongs to a certain domain. The paper finds that the implementation of an SOA system for transactional banking activities can be achieved by rapidly changing the habit of scaling up the system and application. This study shows that banking activity has a huge number of systems that should sync one and another to maintain the data integrity, availability, interoperability, and composability of the system.

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.

There are multiple steps involved in transitioning a server from the factory to being fully provisioned for an intended workload. These steps include finding the optimal slot for the hardware and to compose the required resources on the hardware for the intended workload. There are many different factors that influence the placement of server hardware in the datacenter, such as physical limitations to connect to a network be it Ethernet or storage networks, power requirements, temperature/cooling considerations, and physical space, etc. In addition to this, there may be custom requirements driven by workload policies (such as security, data privacy, power redundancy, etc.). Once the server has been placed in the right slot it needs to be configured with the appropriate resources for the intended workload. CIED will provide a ranked list of locations for server placement based on the intended workload, connectivity and physical requirements of the server. Once the server is placed in the suggested slot, the solution automatically discovers the server and composes the required resources (compute, storage and networks) for running the appropriate workload. CIED reduces the overall time taken to move hardware from factory to production and also maximizes the server hardware utilization while minimizing downtime by physically placing the resources optimally. From the case study that was undertaken, the time taken to transition a server from factory to being fully provisioned was proportional to the number of devices in the datacenter. With CIED this time is constant irrespective of the complexity or the number of devices in a datacenter.

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.

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.

In this paper, a model-driven software collaborative modeling method is proposed, and a software collaborative modeling framework on model assembly and model updating is established. The composability of models, the integrity of model assembly and the consistency of model updating are studied from two aspects: model assembly mechanism and model updating mechanism. The collaborative modeling protocol is defined, and six collaborative relationship stereotypes are introduced to add collaborative semantics to the model based on the extensible mechanism of UML. The algorithm of model assembly and integrity checking is proposed and the collaborative modeling of multiple clients is implemented. The algorithm of model updating and consistency checking is proposed to solve the problem of model updating in collaborative modeling.