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Composability and modularity in relation to physics are useful properties in the development of cyber-physical systems that interact with their environment. The bond-graph modeling language offers these properties. When systems structures conform to the bond-graph notation, all interfaces are defined as physical “power ports” which are guaranteed to exchange power. Having a single type of interface is a key feature when aiming for modular, composable systems. Furthermore, the facility to monitor energy flows in the system through power ports allows the definition of system-wide properties based on component properties. In this paper we present a metamodel of the bond-graph language aimed to facilitate the description and deployment of software components for cyber-physical systems. This effort provides a formalized description of standardized interfaces that enable physics-conformal interconnections. We present a use-case showing that the metamodel enables composability, reusability, extensibility, replaceability and independence of control software components.

A short overview of some applications of optimal control and category theory in deep neural networks is given.

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.

We propose a heuristics-based social-sensor cloud service selection and composition model to reconstruct mosaic scenes. The proposed approach leverages crowdsourced social media images to create an image mosaic to reconstruct a scene at a designated location and an interval of time. The novel approach relies on the set of features defined on the bases of the image metadata to determine the relevance and composability of services. Novel heuristics are developed to filter out non-relevant services. Multiple machine learning strategies are employed to produce smooth service composition resulting in a mosaic of relevant images indexed by geolocation and time. The preliminary analytical results prove the feasibility of the proposed composition model.

The cloud computing paradigm allows ubiquitous access to resources and computational services. These shared assets can be accessed with limited administrative constraints, can rapidly be configured, and provide a huge shared pool of easily accessible information. Often, a significant portion of the computational services is provided by assets at the edge of these clouds, such as by the computational components of cyber physical systems, resulting in the closely related paradigm of edge computing. If smart devices are used to provide similar functionality at the edge of the cloud, these compositions are often referred to as fog computing. In all these cases, bringing data intensive systems with multi-modality together requires more than technical communications. A common information sphere must allow the homogenous access to heterogeneous information structures, often not simply manifested in different facets and viewpoints, but in conceptually different worldviews. This chapter provides an evaluation of the challenges and a survey of available concepts applicable to cope with these challenges. The central idea is the rigorous separation of propertied concepts and processes that are working on these concepts, allowing to unambiguously identify complementary and competitive views, both needed for the successful application of cyber physical systems in complex environments.

Conventional neural accelerators rely on isolated self-sufficient functional units that perform an atomic operation while communicating the results through an operand delivery-aggregation logic. Each single unit processes all the bits of their operands atomically and produce all the bits of the results in isolation. This paper explores a different design style, where each unit is only responsible for a slice of the bit-level operations to interleave and combine the benefits of bit-level parallelism with the abundant data-level parallelism in deep neural networks. A dynamic collection of these units cooperate at runtime to generate bits of the results, collectively. Such cooperation requires extracting new grouping between the bits, which is only possible if the operands and operations are vectorizable. The abundance of Data-Level Parallelism and mostly repeated execution patterns, provides a unique opportunity to define and leverage this new dimension of Bit-Parallel Vector Composability. This design intersperses bit parallelism within data-level parallelism and dynamically interweaves the two together. As such, the building block of our neural accelerator is a Composable Vector Unit that is a collection of Narrower-Bitwidth Vector Engines, which are dynamically composed or decomposed at the bit granularity. Using six diverse CNN and LSTM deep networks, we evaluate this design style across four design points: with and without algorithmic bitwidth heterogeneity and with and without availability of a high-bandwidth off-chip memory. Across these four design points, Bit-Parallel Vector Composability brings (1.4× to 3.5×) speedup and (1.1× to 2.7×) energy reduction. We also comprehensively compare our design style to the Nvidia's RTX 2080 TI GPU, which also supports INT-4 execution. The benefits range between 28.0× and 33.7× improvement in Performance-per-Watt.

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.

In the past decades, there has been a burst of activity to simplify implementation of complex software systems. The solution framework in software engineering community for this problem is called component-based software design (CBSD), whereas in the modeling and simulation community it is called composability. Composability is a complex feature due to the challenges of creating components, selecting combinations of components, and integrating the selected components. In this paper, we address the challenge through the analysis of Component Selection (CS), the NP-complete process of selecting a minimal set of components to satisfy a set of objectives. Due to the computational complexity of CS, we consider approximation algorithms that make the component selection process practical. We define three variations of CS and present good approximation algorithms to find near optimal solutions. In spite of our creation of approximable variants of Component Selection, we prove that the general Component Selection problem is inapproximable.

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.

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.

Combat system effectiveness simulation (CSES) is a special type of complex system simulation. Three non-functional requirements (NFRs), i.e. model composability, domain specific modeling, and model evolvability, are gaining higher priority from CSES users when evaluating different modeling methodologies for CSES. Traditional CSES modeling methodologies are either domain-neutral (lack of domain characteristics consideration and limited support for model composability) or domain-oriented (lack of openness and evolvability) and fall short of the three NFRs. Inspired by the concept of architecture in systems engineering and software engineering fields, we extend it into a concept of model architecture for complex simulation systems, and propose a model architecture-oriented modeling methodology in which the model architecture plays a central role in achieving the three NFRs. Various model-driven engineering (MDE) approaches and technologies, including simulation modeling platform (SMP), unified modeling language (UML), domain specific modeling (DSM), eclipse modeling framework (EMF), graphical modeling framework (GMF), and so forth, are applied where possible in representing the CSES model architecture and its components' behaviors from physical and cognitive domain aspects. A prototype CSES system, called weapon effectiveness simulation system (WESS), and a non-trivial air-combat simulation example are presented to demonstrate the methodology.

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.

Simulation studies make use of various types of simulation experiments. For users, specifying these experiments is a demanding task since the specification depends on the experiment type and the idiosyncrasies of the used tools. Thus, we present an experiment generation procedure that guides users through the specification process, and abstracts away from the concrete execution environment. This abstraction is achieved by (1) developing schemas that define the properties of simulation experiments and dependencies between them, (2) specifying a mapping between schema properties and template fragments in the specification language of a target backend. We develop schema, template fragments, and mappings for stochastic discrete-event simulation, and show how the concepts can be transported to a different domain of modeling and simulation. Further, we expand the developed "simple" experiment schemas by a schema for experiment designs, and generate executable sensitivity analysis experiments, thereby demonstrating versatility and composability of our approach.

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.

Smart government widely integrates heterogeneous business systems to provide intelligent applications, and improving interdepartmental government coordination capability is a key technical issue that needs to be solved urgently. Service composition technology based on SOA architecture is a good solution. However, due to the inherent heterogeneity and huge scale of smart government, the state space of service composition is very large, which is a great challenge to the efficiency and accuracy of problem solving. Based on analysis of the existing service composition research methods, combined with the domain characteristics of smart government, this paper studies and constructs an abstract government Web service template to reduce the scale of state space for service composition problem. Furthermore, a semantic-based service composability measurement is studied to select service solutions to improve the accuracy.

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.

In today’s Big Data era, data scientists require new computational instruments in order to quickly analyze large-scale datasets using complex codes and quicken the rate of scientific progress. While Federally-funded computer resources, from supercomputers to clouds, are beneficial, they are often limiting - particularly for deep learning and visualization - as they have few Graphics Processing Units (GPUs). GPUs are at the center of modern high-performance computing and artificial intelligence, efficiently performing mathematical operations that can be massively parallelized, speeding up codes used for deep learning, visualization and image processing, more so than general-purpose microprocessors, or Central Processing Units (CPUs). The University of Illinois at Chicago is acquiring a much-in-demand GPU-based instrument, COMPaaS DLV - COMposable Platform as a Service Instrument for Deep Learning & Visualization, based on composable infrastructure, an advanced architecture that disaggregates the underlying compute, storage, and network resources for scaling needs, but operates as a single cohesive infrastructure for management and workload purposes. We are experimenting with a small system and learning a great deal about composability, and we believe COMPaaS DLV users will benefit from the varied workflow that composable infrastructure allows.

Building large resilient and computer systems can be challenging, especially when temporal predictability is also required. Large resilient systems are typically composed of different networking technologies, for example, using different wired and wireless communication technologies. Building such systems with safety-relevant services demands appropriate system interfaces and algorithmic concepts. In this talk we present some design patterns that are useful to build such large resilient and predictable systems. Often, the underlying communication medium does not provide the reliability and real-time properties that are needed for a concrete application. In such a case the presented design patterns can be implemented as a communication layer on top of the underlying communication system. We discuss design patterns for interaction autonomy, system communication and reliability.

While many tailor made card game protocols are known, the vast majority of those lack three important features: mechanisms for distributing nancial rewards and punishing cheaters, composability guarantees and exibility, focusing on the specic game of poker. Even though folklore holds that poker protocols can be used to play any card game, this conjecture remains unproven and, in fact, does not hold for a number of protocols (including recent results). We both tackle the problem of constructing protocols for general card games and initiate a treatment of such protocols in the Universal Composability (UC) framework, introducing an ideal functionality that captures card games that use a set of core card operations. Based on this formalism, we introduce Royale, the rst UC-secure general card games which supports - nancial rewards/penalties enforcement. We remark that Royale also yields the rst UC-secure poker protocol. Interestingly, Royale performs better than most previous works (that do not have composability guarantees), which we highlight through a detailed concrete complexity analysis and benchmarks from a prototype implementation.

Generating good random values described by algebraic data types is often quite intricate. State-of-the-art tools for synthesizing random generators serve the valuable purpose of helping with this task, while providing different levels of invariants imposed over the generated values. However, they are often not built for composability nor extensibility, a useful feature when the shape of our random data needs to be adapted while testing different properties or sub-systems. In this work, we develop an extensible framework for deriving compositional generators, which can be easily combined in different ways in order to fit developers' demands using a simple type-level description language. Our framework relies on familiar ideas from the a la Carte technique for writing composable interpreters in Haskell. In particular, we adapt this technique with the machinery required in the scope of random generation, showing how concepts like generation frequency or terminal constructions can also be expressed in the same type-level fashion. We provide an implementation of our ideas, and evaluate its performance using real-world examples.