The Strict Avalanche Criterion (SAC) is a measure of both confusion and diffusion, which are key properties of a cryptographic hash function. This work provides a working definition of the SAC, describes an experimental methodology that can be used to statistically evaluate whether a cryptographic hash meets the SAC, and uses this to investigate the degree to which compression function of the SHA-1 hash meets the SAC. The results ( @math ) are heartening: SHA-1 closely tracks the SAC after the first 24 rounds, and demonstrates excellent properties of confusion and diffusion throughout.
A simple example clarifies the difference. Babbage @cite_18 uses Lloyd's @cite_5 definition of the SAC and defines a SAC-compliant function:
@cite_5 @cite_18
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The Systeme d'Acquisition des Connaissances Deontique (SACD), part of the Acquisition des Connaissances et Analyse de Textes project, which can generate a knowledge base from the logical structure of regulatory texts, is described. SACD can be used by domain specialists who know the text, characteristics and the intricacies of the legislation, or by knowledge engineers who know SACD and can modify the grammars. SACD performs all syntactic text processing and automatically manipulates the knowledge structures. The specialist interprets all semantic knowledge and performs any tasks requiring domain experience. The knowledge engineer adapts SACD's knowledge structures when the system behaves inaccurately. The logical context of regulatory texts is reviewed, and the text analysis, knowledge base operation, and knowledge base adaptation procedures of SACD are discussed. > We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in the context of Reinforcement Learning (RL). SAC-X enables learning of complex behaviors - from scratch - in the presence of multiple sparse reward signals. To this end, the agent is equipped with a set of general auxiliary tasks, that it attempts to learn simultaneously via off-policy RL. The key idea behind our method is that active (learned) scheduling and execution of auxiliary policies allows the agent to efficiently explore its environment - enabling it to excel at sparse reward RL. Our experiments in several challenging robotic manipulation settings demonstrate the power of our approach. Unlike safety properties which require the absence of a “bad” program trace, k-safety properties stipulate the absence of a “bad” interaction between k traces. Examples of k-safety properties include transitivity, associativity, anti-symmetry, and monotonicity. This paper presents a sound and relatively complete calculus, called Cartesian Hoare Logic (CHL), for verifying k-safety properties. We also present an automated verification algorithm based on CHL and implement it in a tool called DESCARTES. We use DESCARTES to analyze user-defined relational operators in Java and demonstrate that DESCARTES is effective at verifying (or finding violations of) multiple k-safety properties. We investigate the effects of semantically-based crossover operators in genetic programming, applied to real-valued symbolic regression problems. We propose two new relations derived from the semantic distance between subtrees, known as semantic equivalence and semantic similarity. These relations are used to guide variants of the crossover operator, resulting in two new crossover operators--semantics aware crossover (SAC) and semantic similarity-based crossover (SSC). SAC, was introduced and previously studied, is added here for the purpose of comparison and analysis. SSC extends SAC by more closely controlling the semantic distance between subtrees to which crossover may be applied. The new operators were tested on some real-valued symbolic regression problems and compared with standard crossover (SC), context aware crossover (CAC), Soft Brood Selection (SBS), and No Same Mate (NSM) selection. The experimental results show on the problems examined that, with computational effort measured by the number of function node evaluations, only SSC and SBS were significantly better than SC, and SSC was often better than SBS. Further experiments were also conducted to analyse the perfomance sensitivity to the parameter settings for SSC. This analysis leads to a conclusion that SSC is more constructive and has higher locality than SAC, NSM and SC; we believe these are the main reasons for the improved performance of SSC.