Female under-representation in Computing Sciences is a structural problem and hence, solving it requires a profound social change. Indeed, our millenary cultures' collective unconscious contains ingrained position...
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In this paper we survey the literature on geometric and spatial modelling and reasoning in Belief-Desire-Intention (BDI) agents and in logic-programming based approaches. The motivation for this survey is the VEsNA fr...
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Boolean Networks (BNs) are widely used as a modeling formalism in several domains, notably systems biology and computer science. A fundamental problem in BN analysis is the enumeration of trap spaces, which are hyperc...
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We recently proposed Acceleration Driven Clause Learning (ADCL), a novel calculus to analyze satisfiability of Constrained Horn Clauses (CHCs). Here, we adapt ADCL to transition systems and introduce ADCL-NT, a varian...
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ISBN:
(数字)9783031384998
ISBN:
(纸本)9783031384998;9783031384981
We recently proposed Acceleration Driven Clause Learning (ADCL), a novel calculus to analyze satisfiability of Constrained Horn Clauses (CHCs). Here, we adapt ADCL to transition systems and introduce ADCL-NT, a variant for disproving termination. We implemented ADCL-NT in our tool LoAT and evaluate it against the state of the art.
Many software supply chain attacks exploit the fact that what is in a source code repository may not match the artifact that is actually deployed in one's system. This paper describes a logic-based framework that ...
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ISBN:
(纸本)9798400702631
Many software supply chain attacks exploit the fact that what is in a source code repository may not match the artifact that is actually deployed in one's system. This paper describes a logic-based framework that analyzes a software component and its dependencies to determine if they are built in a trustworthy fashion. The properties that are checked include the availability of build provenances and whether the build and deployment process of an artifact is tamper resistant. These properties are based on the open-source community efforts, such as SLSA, that enable an incremental approach to improve supply chain security. We evaluate our tool on the top-30 Java, Python, and npm open-source projects and show that the majority still do not produce provenances. Our evaluation also shows that a large number of open-source Java and Python projects do not have a transparent build platform to produce artifacts, which is a necessary requirement to increase the trust in the published artifacts. We show that our tool fills a gap in the current software supply chain security landscape, and by making it publicly available the open-source community can both benefit from and contribute to it.
Constrained Horn Clauses (CHCs) have recently been studied extensively as a common, uniform foundation for automated program verification. Various program verification problems have been shown to be reducible to CHC s...
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ISBN:
(纸本)9789819983100;9789819983117
Constrained Horn Clauses (CHCs) have recently been studied extensively as a common, uniform foundation for automated program verification. Various program verification problems have been shown to be reducible to CHC solving, and accordingly, CHC solvers have been developed by several research groups. We propose a new optimization method for CHC solving, which reduces the number of predicate arguments by finding (conditional) equality constraints among the predicate arguments. The optimization is especially effective for data-driven CHC solvers such as HoIce, as it significantly reduces the number of data required to infer a solution for CHCs. We have implemented our method and confirmed its effectiveness through experiments.
The complex production processes in modern semiconductor manufacturing involve hundreds of operations on the route of a production lot, so that the period from lot release to completion can stretch over several months...
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ISBN:
(纸本)9783031248405;9783031248412
The complex production processes in modern semiconductor manufacturing involve hundreds of operations on the route of a production lot, so that the period from lot release to completion can stretch over several months. Moreover, high-tech machines performing each of the operations are heterogeneous, may operate on individual wafers, lots or batches of lots in several stages, and require product-specific setups as well as dedicated maintenance procedures. This industrial setting is in sharp contrast to classical job-shop scheduling scenarios, where the production processes and machines are way less diverse and the primary focus is on solving methods for highly combinatorial yet abstract scheduling problems. In this work, we tackle the scheduling of realistic semiconductor manufacturing processes and model their elaborate requirements in hybrid Answer Set programming, taking advantage of difference logic to incorporate machine processing, setup as well as maintenance times. While existing approaches schedule semiconductor manufacturing processes only locally, by applying greedy heuristics or isolatedly optimizing the allocation of particular machine groups, we study the prospects and limitations of scheduling at large scale.
interpretation allows constructing sound static analysis tools by safely approximating program semantics. Frameworks for abstract interpretation typically provide an implementation of a specialized iteration strategy ...
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ISBN:
(纸本)9783031457838;9783031457845
interpretation allows constructing sound static analysis tools by safely approximating program semantics. Frameworks for abstract interpretation typically provide an implementation of a specialized iteration strategy to compute an abstract fixpoint, as well as a number of abstract domains in order to approximate different program properties. However, the design and implementation of additional domains, as well as their combinations, is eventually necessary to successfully prove arbitrary program properties. We propose a rule-based methodology for rapid design and prototyping of new domains and combining existing ones, with a focus on the analysis of logic programs. We provide several examples for domains combining numerical properties and data types and apply them to proving complex program properties.
Sampling over combinatorial spaces is a fundamental problem in artificial intelligence with a wide variety of applications. Since state-of-the-art techniques heavily rely on heuristics whose rigorous analysis remain b...
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Sampling over combinatorial spaces is a fundamental problem in artificial intelligence with a wide variety of applications. Since state-of-the-art techniques heavily rely on heuristics whose rigorous analysis remain beyond the reach of current theoretical tools, the past few years have witnessed interest in the design of techniques to test the quality of samplers. The current state-of-the-art techniques, Barbarik and Barbarik2, focus on the cases where combinatorial spaces are encoded as Conjunctive Normal Form (CNF) formulas. While CNF is a general-purpose form, often techniques rely on exploiting specific representations to achieve speedup. Of particular interest are Horn clauses, which form the basis of the logic programming tools in AI. In this context, a natural question is whether it is possible to design a tester that can determine the correctness of a given Horn sampler. The primary contribution of this paper is an affirmative answer to the above question. We design the first tester, Flash, which tests the correctness of a given Horn sampler: given a specific distribution I and parameters eta, epsilon, and delta, the tester Flash correctly (with probability at least 1 -delta) distinguishes whether the underlying distribution of the Horn-sampler is "epsilon-close" to I or "eta-far" from I by sampling only (O) over tilde (tilt(3) /(eta -epsilon)(4)) samples from the Hornsampler, where the tilt is the ratio of the maximum and the minimum (non-zero) probability masses of I. We also provide a prototype implementation of Flash and test three state-of-the-art samplers on a set of benchmarks.
We present Rhyme, an expressive language designed for high-level data manipulation, with a primary focus on querying and transforming nested structures such as JSON and tensors, while yielding nested structures as out...
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ISBN:
(纸本)9783031520372;9783031520389
We present Rhyme, an expressive language designed for high-level data manipulation, with a primary focus on querying and transforming nested structures such as JSON and tensors, while yielding nested structures as output. Rhyme draws inspiration from a diverse range of declarative languages, including Datalog, JQ, JSONiq, Einstein summation (Einsum), GraphQL, and more recent functional logic programming languages like Verse. It has a syntax that closely resembles existing object notation, is compositional, and has the ability to perform query optimization and code generation through the construction of an intermediate representation (IR). Our IR comprises loop-free and branch-free code with program structure implicitly captured via dependencies. To demonstrate Rhyme's versatility, we implement Rhyme in JavaScript (as an embedded DSL) and illustrate its application across various domains, showcasing its ability to express common data manipulation queries, tensor expressions (a la Einsum), and more.
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