The reactive simulatability (RSIM) framework for asynchronous systems

We define reactive simulatability for general asynchronous systems. Roughly, simulatability means that a real system implements an ideal system (specification) in a way that preserves security in a general cryptographic sense. Reactive means that the system can interact with its users multiple times, e.g., in many concurrent protocol runs or a multi-round game. In terms of distributed systems, reactive simulatability is a type of refinement that preserves particularly strong properties, in particular confidentiality. A core feature of reactive simulatability is composability, i.e., the real system can be plugged in instead of the ideal system within arbitrary larger systems; this is shown in follow-up papers, and so is the preservation of many classes of individual security properties from the ideal to the real systems.A large part of this paper defines a suitable system model. It is based on probabilistic IO automata (PIOA) with two main new features: One is generic distributed scheduling. Important special cases are realistic adversarial scheduling, procedure-call-type scheduling among colocated system parts, and special schedulers such as for fairness, also in combinations. The other is the definition of the reactive runtime via a realization by Turing machines such that notions like polynomial-time are composable. The simple complexity of the transition functions of the automata is not composable.As specializations of this model we define security-specific concepts, in particular a separation between honest users and adversaries and several trust models.The benefit of IO automata as the main model, instead of only interactive Turing machines as usual in cryptographic multi-party computation, is that many cryptographic systems can be specified with an ideal system consisting of only one simple, deterministic IO automaton without any cryptographic objects, as many follow-up papers show. This enables the use of classic formal methods and automatic proof tools for proving larger distributed protocols and systems that use these cryptographic systems.     

Conditional association

Estimating conditional dependence between two random variables given the knowledge of a third random variable is essential in neuroscientific applications to understand the causal architecture of a distributed network. However, existing methods of assessing conditional dependence, such as the conditional mutual information, are computationally expensive, involve free parameters, and are difficult to understand in the context of realizations. In this letter, we discuss a novel approach to this problem and develop a computationally simple and parameter-free estimator. The difference between the proposed approach and the existing ones is that the former expresses conditional dependence in terms of a finite set of realizations, whereas the latter use random variables, which are not available in practice. We call this approach conditional association, since it is based on a generalization of the concept of association to arbitrary metric spaces. We also discuss a novel and computationally efficient approach of generating surrogate data for evaluating the significance of the acquired association value.     

Conditional composition

Generalizing the notion of function composition, we introduce the concept ofconditional function composition and present a theory of such compositions. We use the theory to describe the semantics of a programming language with exceptions, and to relate exceptions to theIF statement.Supported in part by Air Force Office of Scientific Research grant number 91-0070. Now at DEC Systems Research Center, Palo Alto, CA.     

Conditional extremals

Imagine that measurements are made at times t ₀ and t ₁ of the trajectory of a physical system whose governing laws are given approximately by a class ${{\mathcal A}}$ of so-called prior vector fields. Because the physical laws are not known precisely, the measurements might not be realised by the integral curve of any prior field. We want to estimate the behaviour of the physical system between times t ₀ and t ₁. This is done by solving a variational problem, yielding so-called conditional extrema which satisfy an Euler?CLagrange equation. Then conservative prior fields on simply-connected Riemannian manifolds are characterised in terms of their conditional extrema. For specific prior fields on space forms, conditional extrema are obtained in terms of the Weierstrass elliptic function. Another class of examples comes from left-invariant prior fields on bi-invariant Lie groups, whose conditional extrema are shown to be right translations of pointwise-products of 1-parameter subgroups.     

Conditional Capabilities

Protection in capability-based operating systems is comsidered. The concept of a conditional capability, which is a generalization of a conventional capability, is proposed. The conditional capability can only be exercised when certain conditions relating to the context of its use are satisfied. It is shown that such capabilities form a basis upon which features such as domains of protection, revocation, and type extension can be built. The implementation of these features can be isolated into sepuate modules thus leaving the basic protection module uncluttered and simplifying the overall structure of the system.     

Conditional Anomaly Detection

When anomaly detection software is used as a data analysis tool, finding the hardest-to-detect anomalies is not the most critical task. Rather, it is often more important to make sure that those anomalies that are reported to the user are in fact interesting. If too many unremarkable data points are returned to the user labeled as candidate anomalies, the software can soon fall into disuse. One way to ensure that returned anomalies are useful is to make use of domain knowledge provided by the user. Often, the data in question includes a set of environmental attributes whose values a user would never consider to be directly indicative of an anomaly. However, such attributes cannot be ignored because they have a direct effect on the expected distribution of the result attributes whose values can indicate an anomalous observation. This paper describes a general purpose method called conditional anomaly detection for taking such differences among attributes into account, and proposes three different expectation-maximization algorithms for learning the model that is used in conditional anomaly detection. Experiments with more than 13 different data sets compare our algorithms with several other more standard methods for outlier or anomaly detection     

From Conditional Events to Conditional Measures: A New Axiomatic Approach

Our starting point is a definition of conditional event EH which differs from many seemingly similar ones adopted in the relevant literature since 1935, starting with de Finetti. In fact, if we do not assign the same third value u (undetermined) to all conditional events, but make it depend on EH, it turns out that this function t(EH) can be taken as a general conditional uncertainty measure, and we get (through a suitable – in a sense, compulsory – choice of the relevant operations among conditional events) the natural axioms for many different (besides probability) conditional measures.     

Conditional matching preclusion sets

The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. For many interconnection networks, the optimal sets are precisely those induced by a single vertex. In this paper, we look for obstruction sets beyond these sets. We introduce the conditional matching preclusion number of a graph. It is the minimum number of edges whose deletion results in a graph with no isolated vertices that has neither perfect matchings nor almost-perfect matchings. We find this number and classify all optimal sets for several basic classes of graphs.     

Conditional e-payments with transferability

We introduce a novel conditional e-cash protocol allowing future anonymous cashing of bank-issued e-money only upon the satisfaction of an agreed-upon public condition. Payers are able to remunerate payees for services that depend on future, yet to be determined outcomes of events. Moreover, payees are able to further transfer payments to third parties. Once the payment is complete, any double-spending attempt by the payer will reveal its identity; no double spending by any of payees in the payee transfer chain is possible. Payers cannot be linked to payees or to ongoing or past transactions. The flow of cash within the system is thus both correct and anonymous. We discuss several applications of conditional e-cash including online trading of financial securities, prediction markets, and betting systems.     

Conditional probability and fuzzy information

The main subject of this paper is the embedding of fuzzy set theory—and related concepts—in a coherent conditional probability scenario. This allows to deal with perception-based information—in the sense of Zadeh—and with a rigorous treatment of the concept of likelihood, dealing also with its role in statistical inference. A coherent conditional probability is looked on as a general non-additive “uncertainty” measure m(·)=P(E|·) of the conditioning events. This gives rise to a clear, precise and rigorous mathematical frame, which allows to define fuzzy subsets and to introduce in a very natural way the counterparts of the basic continuous T-norms and the corresponding dual T-conorms, bound to the former by coherence. Also the ensuing connections of this approach to possibility theory and to information measures are recalled.     

Conditional edge-fault-tolerant Hamiltonicity of dual-cubes

The dual-cube is an interconnection network for linking a large number of nodes with a low node degree. It uses low-dimensional hypercubes as building blocks and keeps the main desired properties of the hypercube. A dual-cube DC(n) has n + 1 links per node where n is the degree of a cluster (n-cube), and one more link is used for connecting to a node in another cluster. In this paper, assuming each node is incident with at least two fault-free links, we show a dual-cube DC(n) contains a fault-free Hamiltonian cycle, even if it has up to 2n − 3 link faults. The result is optimal with respect to the number of tolerant edge faults.     

Conditional and composite temporal CSPs

Constraint Satisfaction Problems (CSPs) have been widely used to solve combinatorial problems. In order to deal with dynamic CSPs where the information regarding any possible change is known a priori and can thus be enumerated beforehand, conditional constraints and composite variables have been studied in the past decade. Indeed, these two concepts allow the addition of variables and their related constraints in a dynamic manner during the resolution process. More precisely, a conditional constraint restricts the participation of a variable in a feasible scenario while a composite variable allows us to express a disjunction of variables where only one will be added to the problem to solve. In order to deal with a wide variety of real life applications under temporal constraints, we present in this paper a unique temporal CSP framework including numeric and symbolic temporal information, conditional constraints and composite variables. We call this model, a Conditional and Composite Temporal CSP (or CCTCSP). To solve the CCTCSP we propose two methods respectively based on Stochastic Local Search (SLS) and constraint propagation. In order to assess the efficiency in time of the solving methods we propose, experimental tests have been conducted on randomly generated CCTCSPs. The results demonstrate the superiority of a variant of the Maintaining Arc Consistency (MAC) technique (that we call MAX+) over the other constraint propagation strategies, Forward Checking (FC) and its variants, for both consistent and inconsistent problems. It has also been shown that, in the case of consistent problems, MAC+ outperforms the SLS method Min Conflict Random Walk (MCRW) for highly constrained CCTCSPs while both methods have comparable time performance for under and middle constrained problems. MCRW is, however, the method of choice for highly constrained CCTCSPs if we decide to trade search time for the quality of the solution returned (number of solved constraints).     

Aggregate Queries Over Conditional Tables

Conditional tables have been identified long ago as a way to capture unknown or incomplete information. However, queries over conditional tables have never been allowed to involve column functions such as aggregates. In this paper, the theory of conditional tables is extended in this direction, and it is shown that a strong representation system exists which has the closure property that the result of an aggregate query over a conditional table can be again represented by a conditional table. It turns out, however, that the number of tuples in a conditional table representing the result of an aggregate query may grow exponentially in the number of variables in the table. This phenomenon is analyzed in detail, and tight upper and lower bounds concerning the number of tuples contained in the result of an aggregate query are given. Finally, representation techniques are sketched that approximate aggregation results in tables of reasonable size.     

Conditional Probability Meets Update Logic

Dynamic update of information states is a new paradigm in logicalsemantics. But such updates are also a traditional hallmark ofprobabilistic reasoning. This note brings the two perspectives togetherin an update mechanism for probabilities which modifies state spaces.     

Model-Based Diagnosis and Conditional Logic

The model-based diagnostic approach was first introduced to overcome the limitations of heuristic systems. However, research on model-based systems showed that the model-based diagnosis approaches resort to assumptions that can be viewed as the return, though controlled, of heuristics into diagnostic reasoning. In this paper we focus on diagnosis with component-oriented device models. We argue for the need to represent and reason with these assumptions. We present a conditional logic, DL, That is suitable for diagnostic reasoning and allows us to represent and reason with assumptions.     

Conditional edge-fault-tolerant edge-bipancyclicity of hypercubes

The hypercube has been one of the most popular interconnection networks for parallel computer/communication systems. In this paper, we assume that each node is incident with at least two fault-free links. Under this assumption, we show that every fault-free edge lies on a fault-free cycle of every even length from 6 to 2ⁿ inclusive, even if it has up to 2n − 5 link faults. The result is optimal with respect to the number of link faults tolerated.     

On Coherent Conditional Probabilities and Disintegrations

Existence of coherent extensions of coherent conditional probabilities is one of the major merits of de Finetti's theory of probability. However, coherent extensions which meet some special property, like -additivity or disintegrability, can fail to exist. An example is given where a coherent and -additive conditional probability cannot be extended preserving both -additivity and coherence. Motivated by such example, conditions are provided in order that a coherent and -additive conditional probability admits a coherent and -additive extension. Moreover, conditions are given for the existence of disintegrations, possibly -additive, of a probability along a partition.     

Conditional states and independence in D-posets

In this paper a new approach to a conditional probability is studied in more general structure called a D-poset. The authors go into the inner structure of a conditional system which is a crucial notion for the existence of a conditional state. An independence of elements in a D-poset with respect to a state is defined.     

Evaluation Metrics for Conditional Image Generation

International Journal of Computer Vision - We present two new metrics for evaluating generative models in the class-conditional image generation setting. These metrics are obtained by generalizing...