Improvement

Résumé

Improvement is the process of a thing moving from one state to a state considered to be better, usually through some action intended to bring about that better state. The concept of improvement is important to governments and businesses, as well as to individuals. History of the concept The term "improvement" in general means "gradual, piecemeal, but cumulative betterment", which can refer to both individuals and societies as a whole. The term "improvement" historically referred to land improvement, the process of making wildland more suitable for human uses, particularly the cultivation of crops. Agricultural writers contrasted "improvement" with the traditional custom that governed farming practices at the time. The belief in agricultural "improvement" was the belief that the earth could be made more fruitful. More specifically, it was the belief that "the knowledge of nature would allow the best possible use of resources". It emerged in late medieval England and later shap

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https://en.wikipedia.org/wiki/Improvement

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Publications associées (11)

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Cours associés (5)

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Malicious Traffic Detection in Local Networks with Snort

Loïc Etienne

Snort is an open source Network Intrusion Detection System combining the benefits of signature, protocol and anomaly based inspection and is considered to be the most widely de- ployed IDS/IPS technology worldwide. However, Snort's deployment in a large corporate network poses different problems in terms of performance or rule selection. This paper proposes different improvements to the Snort Security Platform: the use of another library is proposed to significantly improve the amount of traffic that can be analyzed, and Snort's multithreading possibilities are explored. A new rule classification has been devised, and rulesets suited to large corporate networks are proposed. The use of Oinkmaster has been tested and documented to seamlessly update Snort's rules.

2009

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Safe non-smooth black-box optimization with application to policy search

Andreas Krause, Ilnura Usmanova

For safety-critical black-box optimization tasks, observations of the constraints and the objective are often noisy and available only for the feasible points. We propose an approach based on log barriers to find a local solution of a non-convex non-smooth black-box optimization problem minf0(x)minf0(x)\min f^0(x) subject to fi(x)≤0,i=1,…,mfi(x)≤0,i=1,…,mf^i(x)\leq 0, i = 1,\ldots, m, at the same time, guaranteeing constraint satisfaction while learning with high probability. Our proposed algorithm exploits noisy observations to iteratively improve on an initial safe point until convergence. We derive the convergence rate and prove safety of our algorithm. We demonstrate its performance in an application to an iterative control design problem.

PMLR2020

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The aim of this paper is to describe how the combination of speaker verification algorithms with a priori decision thresholds can improve the overall robustness of a real application. The evaluation is performed in the context of a field application where each client is verified from a 7 digit pin code. This paper demonstrate that it is possible to increase the global performances of the system on combining the result of several algorithms.

IDIAP1996

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