Relevant bibliographies by topics / Ruleset

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Ruleset'

Author: Grafiati
Published: 19 February 2023

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Journal articles on the topic "Ruleset"

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Díaz-Verdejo, Jesús, Javier Muñoz-Calle, Antonio Estepa Alonso, Rafael Estepa Alonso, and Germán Madinabeitia. "On the Detection Capabilities of Signature-Based Intrusion Detection Systems in the Context of Web Attacks." Applied Sciences 12, no. 2 (January 14, 2022): 852. http://dx.doi.org/10.3390/app12020852.

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Signature-based Intrusion Detection Systems (SIDS) play a crucial role within the arsenal of security components of most organizations. They can find traces of known attacks in the network traffic or host events for which patterns or signatures have been pre-established. SIDS include standard packages of detection rulesets, but only those rules suited to the operational environment should be activated for optimal performance. However, some organizations might skip this tuning process and instead activate default off-the-shelf rulesets without understanding its implications and trade-offs. In this work, we help gain insight into the consequences of using predefined rulesets in the performance of SIDS. We experimentally explore the performance of three SIDS in the context of web attacks. In particular, we gauge the detection rate obtained with predefined subsets of rules for Snort, ModSecurity and Nemesida using seven attack datasets. We also determine the precision and rate of alert generated by each detector in a real-life case using a large trace from a public webserver. Results show that the maximum detection rate achieved by the SIDS under test is insufficient to protect systems effectively and is lower than expected for known attacks. Our results also indicate that the choice of predefined settings activated on each detector strongly influences its detection capability and false alarm rate. Snort and ModSecurity scored either a very poor detection rate (activating the less-sensitive predefined ruleset) or a very poor precision (activating the full ruleset). We also found that using various SIDS for a cooperative decision can improve the precision or the detection rate, but not both. Consequently, it is necessary to reflect upon the role of these open-source SIDS with default configurations as core elements for protection in the context of web attacks. Finally, we provide an efficient method for systematically determining which rules deactivate from a ruleset to significantly reduce the false alarm rate for a target operational environment. We tested our approach using Snort’s ruleset in our real-life trace, increasing the precision from 0.015 to 1 in less than 16 h of work.
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Okajima, Yuzuru, and Kunihiko Sadamasa. "Deep Neural Networks Constrained by Decision Rules." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2496–505. http://dx.doi.org/10.1609/aaai.v33i01.33012496.

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Deep neural networks achieve high predictive accuracy by learning latent representations of complex data. However, the reasoning behind their decisions is difficult for humans to understand. On the other hand, rule-based approaches are able to justify the decisions by showing the decision rules leading to them, but they have relatively low accuracy. To improve the interpretability of neural networks, several techniques provide post-hoc explanations of decisions made by neural networks, but they cannot guarantee that the decisions are always explained in a simple form like decision rules because their explanations are generated after the decisions are made by neural networks.In this paper, to balance the accuracy of neural networks and the interpretability of decision rules, we propose a hybrid technique called rule-constrained networks, namely, neural networks that make decisions by selecting decision rules from a given ruleset. Because the networks are forced to make decisions based on decision rules, it is guaranteed that every decision is supported by a decision rule. Furthermore, we propose a technique to jointly optimize the neural network and the ruleset from which the network select rules. The log likelihood of correct classifications is maximized under a model with hyper parameters about the ruleset size and the prior probabilities of rules being selected. This feature makes it possible to limit the ruleset size or prioritize human-made rules over automatically acquired rules for promoting the interpretability of the output. Experiments on datasets of time-series and sentiment classification showed rule-constrained networks achieved accuracy as high as that achieved by original neural networks and significantly higher than that achieved by existing rule-based models, while presenting decision rules supporting the decisions.
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Li, Chenglong, Tao Li, Junnan Li, Zilin Shi, and Baosheng Wang. "Enabling Packet Classification with Low Update Latency for SDN Switch on FPGA." Sustainability 12, no. 8 (April 11, 2020): 3068. http://dx.doi.org/10.3390/su12083068.

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Field Programmable Gate Array (FPGA) is widely used in real-time network processing such as Software-Defined Networking (SDN) switch due to high performance and programmability. Bit-Vector (BV)-based approaches can implement high-performance multi-field packet classification, on FPGA, which is the core function of the SDN switch. However, the SDN switch requires not only high performance but also low update latency to avoid controller failure. Unfortunately, the update latency of BV-based approaches is inversely proportional to the number of rules, which means can hardly support the SDN switch effectively. It is reasonable to split the ruleset into sub-rulesets that can be performed in parallel, thereby reducing update latency. We thus present SplitBV for the efficient update by using several distinguishable exact-bits to split the ruleset. SplitBV consists of a constrained recursive algorithm for selecting the bit positions that can minimize the latency and a hybrid lookup pipeline. It can achieve a significant reduction in update latency with negligible memory growth and comparable high performance. We implement SplitBV and evaluate its performance by simulation and FPGA prototype. Experimental results show that our approach can reduce 73% and 36% update latency on average for synthetic 5-tuple rules and OpenFlow rules respectively.
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Khummanee, Suchart, Umaporn Saisangchan, Kritsanapong Somsuk, and Sarutte Atsawaraungsuk. "High-speed Firewall Rule Verification Technique Improves Throughput Performance for IP Version 6." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 16, no. 3 (August 27, 2022): 338–51. http://dx.doi.org/10.37936/ecti-cit.2022163.248690.

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Throughput performance of firewalls depend on the execution speed to verify rules. Internet Protocol Version 6 (IPv6) and IPv4 ruleset memory requirements differ and affect rule access and execution time in a wide range of common firewalls. This paper contributes a high-speed firewall to execute rules for IPv6 with constant O(1) access time, and consumes optimal O(nbit) memory for 64-bit architectures, named FW6 firewall. Results are based on actual performance evaluations in conjunction with other high-speed firewalls (IPSets, IPack, and F3), such as processing time, memory consumption and throughput. Throughput measurements in IPv6 TCP/UDP packet trials (across ruleset and window sizes) show FW6 significantly outperforms IPSets. The trials have shown that FW6 improves throughput performance over IPSets by 0.24% (mean) and 0.21% (median) across all test variables. Nevertheless, the results suggest similarity and a minor performance increase by FW6 over IPSets. In addition, FW6 and IPSets throughputs are similar to IPack and F3 in IPv4 ruleset execution comparisons. As a result, FW6 can be used to replace previous high-speed firewalls.
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Holeňa, Martin. "Measures of ruleset quality for general rules extraction methods." International Journal of Approximate Reasoning 50, no. 6 (June 2009): 867–79. http://dx.doi.org/10.1016/j.ijar.2009.03.002.

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Li, Yifei, Jinlin Wang, Xiao Chen, and Jinghong Wu. "SplitTrie: A Fast Update Packet Classification Algorithm with Trie Splitting." Electronics 11, no. 2 (January 10, 2022): 199. http://dx.doi.org/10.3390/electronics11020199.

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Software Defined Network (SDN) currently is widely used in the implementation of new network technologies owing to its distinctive advantages. In changeable SDN environments, the update performance of SDN switches has significant importance for the overall network performance because packet processing could be interrupted by ruleset updating in SDN switches. In order to guarantee high update performance, we propose a new classification algorithm, SplitTrie, based on trie structures and trie splitting. SplitTrie splits rulesets according to the field type vectors of rules. The splitting can improve the update performance because it reduces the trie structure sizes. Experimental results demonstrated that SplitTrie could achieve 20 times of update speed in the complex rulesets comparing the method without trie splitting.
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Caviness, Kenneth E., Camille Morrow, Christen Case, and Victoria Kratzke. "An Improved Generalized Enumeration of Substitution Systems." Complex Systems 29, no. 4 (December 15, 2020): 837–60. http://dx.doi.org/10.25088/complexsystems.29.4.837.

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The enumeration of all sequential substitution system rulesets is modified to include generalized substitution system rulesets. Unlike its predecessor, the new enumeration is one-to-one: each ruleset is guaranteed to appear exactly once in the new enumeration, which moreover possesses an elegant simplicity that allows jumps over increasingly longer undesired subsequences of many types. This process effectively results in an increased acceleration and greatly improved performance.
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Rijati, Nova, Diana Purwitasar, Surya Sumpeno, and Mauridhi Hery Purnomo. "A Rule-Generation Model for Class Imbalances to Detect Student Entrepreneurship Based on the Theory of Planned Behavior." Cybernetics and Information Technologies 22, no. 2 (June 1, 2022): 160–78. http://dx.doi.org/10.2478/cait-2022-0023.

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Abstract The ability to identify the entrepreneurial potential of students enables higher education institutions to contribute to the economic and social development of a country. Current research trends regarding the detection of student entrepreneurial potential have the greatest challenge in the unequal ratio of datasets. This study proposes a rule-generation model in an imbalanced situation to classify student entrepreneurship based on the Theory of Planned Behavior (TPB). The result is a ruleset that is used for the early detection of student entrepreneurial potential. The proposed method consists of three main stages, namely preprocessing data to classify data based on TPB variables, generating a dataset by clustering and selecting attributes by sampling to balance the data, and finally generating a ruleset. Furthermore, the results of the detecting ruleset have been evaluated with actual data from the student tracer study as ground truth. The evaluation results show high accuracy so that the ruleset can be applied to the higher education environment in the future.
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Han, Yo-Sub, and Hwee Kim. "Ruleset optimization on isomorphic oritatami systems." Theoretical Computer Science 785 (September 2019): 128–39. http://dx.doi.org/10.1016/j.tcs.2019.03.020.

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Gloster, Annabella. "Disaster Rules Rob Russell , TimothyHodgetts , PeterMahoney Disaster Ruleset alWiley Blackwell£24.99176pp97814051937881405193786." Emergency Nurse 18, no. 10 (March 8, 2011): 9. http://dx.doi.org/10.7748/en.18.10.9.s11.

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Dissertations / Theses on the topic "Ruleset"

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Haq, Ikram. "Fraud detection for online banking for scalable and distributed data." Thesis, Federation University Australia, 2020. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/171977.

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Online fraud causes billions of dollars in losses for banks. Therefore, online banking fraud detection is an important field of study. However, there are many challenges in conducting research in fraud detection. One of the constraints is due to unavailability of bank datasets for research or the required characteristics of the attributes of the data are not available. Numeric data usually provides better performance for machine learning algorithms. Most transaction data however have categorical, or nominal features as well. Moreover, some platforms such as Apache Spark only recognizes numeric data. So, there is a need to use techniques e.g. One-hot encoding (OHE) to transform categorical features to numerical features, however OHE has challenges including the sparseness of transformed data and that the distinct values of an attribute are not always known in advance. Efficient feature engineering can improve the algorithm’s performance but usually requires detailed domain knowledge to identify correct features. Techniques like Ripple Down Rules (RDR) are suitable for fraud detection because of their low maintenance and incremental learning features. However, high classification accuracy on mixed datasets, especially for scalable data is challenging. Evaluation of RDR on distributed platforms is also challenging as it is not available on these platforms. The thesis proposes the following solutions to these challenges: • We developed a technique Highly Correlated Rule Based Uniformly Distribution (HCRUD) to generate highly correlated rule-based uniformly-distributed synthetic data. • We developed a technique One-hot Encoded Extended Compact (OHE-EC) to transform categorical features to numeric features by compacting sparse-data even if all distinct values are unknown. • We developed a technique Feature Engineering and Compact Unified Expressions (FECUE) to improve model efficiency through feature engineering where the domain of the data is not known in advance. • A Unified Expression RDR fraud deduction technique (UE-RDR) for Big data has been proposed and evaluated on the Spark platform. Empirical tests were executed on multi-node Hadoop cluster using well-known classifiers on bank data, synthetic bank datasets and publicly available datasets from UCI repository. These evaluations demonstrated substantial improvements in terms of classification accuracy, ruleset compactness and execution speed.
Doctor of Philosophy
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Von, Drasek Nathan James. "Return to Earth: Decayed Rulesets in VR." Miami University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=miami1626166928722007.

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McNair, Lynn Jacqueline. "'Rules, rules, rules and we're not allowed to skip'." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/22942.

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Despite the breadth of research on the educational transition of young children, there has been little evidence, in Scotland, of this knowledge impacting on every day practice. The overall contention that emerges from the literature is that some children positively embrace the experience, while others face challenges and risk failure and regression. There is a need for research into the transition from the early years setting to school, which holds promise that the findings will be disseminated to stakeholders locally, nationally and internationally with the aim that the perspectives of young children are heard. This ethnographic study is an examination of the perspectives of 16 young children as they transition from an early years centre, Lilybank, to four primary schools, Northfield, Southfield, Eastfield and Westfield, in a Scottish city. Seven key qualitative questions were asked which explored how children, parents and professionals experienced this educational transition. Data was gathered from empirical methods such as participant observations, mind–mapping sessions, interviews and documentation – e.g., council procedures and school handbooks. Participant observations took place in the early years setting and the participating schools. Most of the interviews took place in the children’s homes, or in a convenient environment for the family, such as a local cafeteria. An analysis of the data shows that power is a central concept in understanding transitions. The voices of children, and their families, are often silenced by policy-makers, bureaucrats and professionals during the process, or overshadowed and undermined by mainstream procedures. Children are expected to become acquiescent, adjusting to coercive practices used in the school institution. However, the findings also show that some children find ways to creatively resist organisation. Unique life journeys involve differences and from their individual experiences, children construct elaborate knowledge. The views of children can (and do) add nuance to our understanding of how power impacts on their transition experience. Children’s accounts of discipline strategies used by the schools were insightful. The concept of power is under-theorised and under-explored in transitions. This study, therefore, adds to the growing body of transition research. Further, the findings of this study stress the need for policy makers and institutions to reflect on and question the complex role of power in young children’s transitions.
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Figueira, Lucas Baggio. "Sobre o modelo neural RuleNet e suas características simbólica e cooperativa." Universidade Federal de São Carlos, 2004. https://repositorio.ufscar.br/handle/ufscar/566.

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Made available in DSpace on 2016-06-02T19:06:13Z (GMT). No. of bitstreams: 1 DissLBF.pdf: 823769 bytes, checksum: 521bc3ae1780ad06aa0fbb01ae4c2d40 (MD5) Previous issue date: 2004-05-28
Financiadora de Estudos e Projetos
Machine learning is an area of Artificial Intelligence that deals with methods and techniques for implementing automatic learning in computational systems. This research work investigates a machine learning neural model called RuleNet and its extension for fuzzy domains named Fuzzy RuleNet. Among the advantages of the RuleNet proposal are its simplicity, easiness and fast training as well as the way it represents the induced concept, which can be characterized as symbolic. This aspect makes RuleNet suitable for participating in cooperative systems. This research work investigates both the contribution of the RuleNet model as a stand alone learning technique as well as part of a cooperative system. It presents and discusses the results obtained in several experiments, evaluating RuleNet as a stand alone machine learning (versus two other machine learning methods, the ID3 and the NGE) and as part of a cooperative system, articulated to ID3 and to NGE.
Aprendizado de máquina é uma área da Inteligência Artificial que investe na pesquisa de métodos e técnicas para viabilizar o aprendizado automático em sistemas computacionais. Este trabalho de pesquisa investiga um modelo neural de aprendizado de máquina chamado RuleNet e sua extensão Fuzzy RuleNet, para domínios fuzzy. Dentre as vantagens da proposta RuleNet estão sua simplicidade, facilidade e rapidez no treinamento bem como a maneira como representa o conceito induzido, que pode ser caracterizada como simbólica. Esse aspecto torna o RuleNet adequado a ser incorporado a sistemas cooperativos de aprendizado. O trabalho de pesquisa investiga a contribuição do modelo RuleNet tanto como uma técnica de aprendizado stand-alone quanto como parte de um sistema cooperativo. O trabalho apresenta e discute os resultados obtidos em vários experimentos que avaliam o RuleNet como método de aprendizado stand-alone (versus dois outros métodos de aprendizado de máquina, o ID3 e o NGE) e como parte de um sistema cooperativo, articulado tanto ao ID3 quanto ao NGE.
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Soto, Raymond. "First Rules." Digital Commons at Loyola Marymount University and Loyola Law School, 1991. https://digitalcommons.lmu.edu/etd/636.

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Bjöörn, Christopher, and Jacob Johnsson. "Universe-defining rules." Thesis, Blekinge Tekniska Högskola, Institutionen för teknik och estetik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4938.

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Abstrakt I detta arbete undersöks hur konceptet lek går att applicera på digitala spel och hur man presenterar ett fiktivt universum och de regler som definierar det universumet. Syftet med denna undersökning är att öka kvaliteten på digitala spel för spelare genom att öka förståelsen för hur sådana regler introduceras. Frågeställningen som ämnas att besvaras är “hur kan man introducera realistiska, semi-realistiska och fiktiva regler i ett spel?”. Undersökningen baseras delvis på analyser kring varför vissa introduktioner av regler ofta accepteras och andra inte, dels på utvärdering av en gestaltning och dels på tidigare forskning. Denna undersökning är indelad i två delar; en researchdel och en produktionsdel. För att besvara frågan har research skett kring vad som känns till sedan tidigare och ett digitalt spel har producerats där den stora regeln som skiljer verkligheten från detta fiktiva universum är paranormal aktivitet, eller spöken. Nyckelord: regler, magisk cirkel, inlevelse och spelproduktion. Abstract In this work the concept of play and how it may be applied to digital games and how to introduce a fictional universe and the rules that define that universe is being investigated. The purpose of this work is to increase the quality of digital games by increasing our understanding of how such rules may be introduced. The question to be answered is “how may realistic, semi-realistic and fictional rules be introduced in a digital game?”. This work is based partly on analyses on why some introductions of rules are often accepted and some often not, partly on evaluation of a product created by us and partly on earlier research. This work is split into two parts; one research part and one production part. To answer the question research about what is previously known has been conducted and a digital game has been produced where the main rule that separates the fictional universe from ours is paranormal activity, or ghosts. Keywords: Rules, magical circle, immersion and game production.
Detta är en reflektionsdel till en digital medieproduktion.
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Anderson, Grant. "Random Relational Rules." The University of Waikato, 2008. http://hdl.handle.net/10289/2562.

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In the field of machine learning, methods for learning from single-table data have received much more attention than those for learning from multi-table, or relational data, which are generally more computationally complex. However, a significant amount of the world's data is relational. This indicates a need for algorithms that can operate efficiently on relational data and exploit the larger body of work produced in the area of single-table techniques. This thesis presents algorithms for learning from relational data that mitigate, to some extent, the complexity normally associated with such learning. All algorithms in this thesis are based on the generation of random relational rules. The assumption is that random rules enable efficient and effective relational learning, and this thesis presents evidence that this is indeed the case. To this end, a system for generating random relational rules is described, and algorithms using these rules are evaluated. These algorithms include direct classification, classification by propositionalisation, clustering, semi-supervised learning and generating random forests. The experimental results show that these algorithms perform competitively with previously published results for the datasets used, while often exhibiting lower runtime than other tested systems. This demonstrates that sufficient information for classification and clustering is retained in the rule generation process and that learning with random rules is efficient. Further applications of random rules are investigated. Propositionalisation allows single-table algorithms for classification and clustering to be applied to the resulting data, reducing the amount of relational processing required. Further results show that techniques for utilising additional unlabeled training data improve accuracy of classification in the semi-supervised setting. The thesis also develops a novel algorithm for building random forests by making efficient use of random rules to generate trees and leaves in parallel.
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Linnik, Y. N. "Sales promotion rules." Thesis, Вид-во СумДУ, 2005. http://essuir.sumdu.edu.ua/handle/123456789/19899.

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Nalenz, Malte. "Horseshoe RuleFit : Learning Rule Ensembles via Bayesian Regularization." Thesis, Linköpings universitet, Statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-130249.

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This work proposes Hs-RuleFit, a learning method for regression and classification, which combines rule ensemble learning based on the RuleFit algorithm with Bayesian regularization through the horseshoe prior. To this end theoretical properties and potential problems of this combination are studied. A second step is the implementation, which utilizes recent sampling schemes to make the Hs-RuleFit computationally feasible. Additionally, changes to the RuleFit algorithm are proposed such as Decision Rule post-processing and the usage of Decision rules generated via Random Forest. Hs-RuleFit addresses the problem of finding highly accurate and yet interpretable models. The method shows to be capable of finding compact sets of informative decision rules that give a good insight in the data. Through the careful choice of prior distributions the horse-shoe prior shows to be superior to the Lasso in this context. In an empirical evaluation on 16 real data sets Hs-RuleFit shows excellent performance in regression and outperforms the popular methods Random Forest, BART and RuleFit in terms of prediction error. The interpretability is demonstrated on selected data sets. This makes the Hs-RuleFit a good choice for science domains in which interpretability is desired. Problems are found in classification, regarding the usage of the horseshoe prior and rule ensemble learning in general. A simulation study is performed to isolate the problems and potential solutions are discussed. Arguments are presented, that the horseshoe prior could be a good choice in other machine learning areas, such as artificial neural networks and support vector machines.
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Zhang, Haoning. "Indexed semantic mapping rules." Zurich : ETH, Swiss Federal Institut of Technology, Institute of Information Systems, 2009. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=449.

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Books on the topic "Ruleset"

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Doern, G. Bruce, and Robert Johnson, eds. Rules, Rules, Rules, Rules. Toronto: University of Toronto Press, 2006. http://dx.doi.org/10.3138/9781442679504.

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Querales, Juandemaro. Ruleteo: Novela. La Victoria, Venezuela: Editorial Berkana, 1999.

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Scandora, Julie Moss. Rules are rules. Bothwell, WA: Book Publishers Network, 2008.

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Tom, Watson. The rulesof golf. London: Hodder & Stoughton, 1988.

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Németh, István Z. Rulett: Versek. Somorja: Méry Ratio, 2001.

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Commission, Wyoming Community College. Rules. Cheyenne, WY (2301 Central Ave., Cheyenne 82002): The Commission, 1986.

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Lord, Cynthia. Rules. New York: Scholastic Press, 2006.

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Commission, Ohio Civil Rights. Rules. Columbus, Ohio (220 Parsons Ave., Columbus 43215): The Commission, 1986.

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Rules. New York: Scholastic, 2007.

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Rules. New York: Scholastic Press, 2008.

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Book chapters on the topic "Ruleset"

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Han, Yo-Sub, and Hwee Kim. "Ruleset Optimization on Isomorphic Oritatami Systems." In Lecture Notes in Computer Science, 33–45. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66799-7_3.

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Gao, Yang, Lei Wu, and Joshua Zhexue Huang. "Ensemble Learning Classifier System and Compact Ruleset." In Lecture Notes in Computer Science, 42–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11903697_6.

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Holeňa, Martin. "Measures of Ruleset Quality Capable to Represent Uncertain Validity." In Lecture Notes in Computer Science, 430–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75256-1_39.

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Dixon, Phillip William, Dawid Wolfe Corne, and Martin John Oates. "A Ruleset Reduction Algorithm for the XCS Learning Classifier System." In Lecture Notes in Computer Science, 20–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-40029-5_2.

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Doern, G. Bruce, and Robert Johnson. "1 Multilevel Regulatory Governance: Concepts, Context, and Key Issues." In Rules, Rules, Rules, Rules, edited by G. Bruce Doern and Robert Johnson, 1–26. Toronto: University of Toronto Press, 2006. http://dx.doi.org/10.3138/9781442679504-003.

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de Buen Rodriguez, Pablo R., Eduardo F. Morales, and Sunil Vadera. "RuLess: A Method for the Acquisition and Simplification of Rules." In Lecture Notes in Computer Science, 272–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/10720076_25.

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Wilson, Stewart W. "Compact Rulesets from XCSI." In Advances in Learning Classifier Systems, 197–208. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-48104-4_12.

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Luard, Evan. "Rules." In The Balance of Power, 305–30. London: Palgrave Macmillan UK, 1992. http://dx.doi.org/10.1007/978-1-349-21927-8_12.

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Thomä, Helmut, and Horst Kächele. "Rules." In Psychoanalytic Practice, 215–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-71430-6_7.

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Thomä, Helmut, and Horst Kächele. "Rules." In Psychoanalytic Practice, 250–313. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-71878-6_7.

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Conference papers on the topic "Ruleset"

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Liang, P., and J. F. Roddick. "RPL: a ruleset pattern language." In International Conference on Artificial Intelligence and Industrial Application. Southampton, UK: WIT Press, 2015. http://dx.doi.org/10.2495/aiia140511.

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Ulltveit-Moe, Nils, and Vladimir Oleshchuk. "Privacy Violation Classification of Snort Ruleset." In 2010 18th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). IEEE, 2010. http://dx.doi.org/10.1109/pdp.2010.87.

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Piscitello, Andrea, Alessandro A. Nacci, Vincenzo Rana, Marco D. Santambrogio, and Donatella Sciuto. "Ruleset Minimization in Multi-tenant Smart Buildings." In 2016 19th IEEE Intl Conference on Computational Science and Engineering (CSE), IEEE 14th Intl Conference on Embedded and Ubiquitous Computing (EUC), and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES). IEEE, 2016. http://dx.doi.org/10.1109/cse-euc-dcabes.2016.164.

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Naghmouchi, Jamin, Daniele Paolo Scarpazza, and Mladen Berekovic. "Small-ruleset regular expression matching on GPGPUs." In the 24th ACM International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1810085.1810130.

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Cook, Michael, Simon Colton, and Azalea Raad. "Inferring Design Constraints From Game Ruleset Analysis." In 2018 IEEE Conference on Computational Intelligence and Games (CIG). IEEE, 2018. http://dx.doi.org/10.1109/cig.2018.8490412.

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Kim, Hyungseok, Sukjun Ko, Dong Seong Kim, and Huy Kang Kim. "Firewall ruleset visualization analysis tool based on segmentation." In 2017 IEEE Symposium on Visualization for Cyber Security (VizSec). IEEE, 2017. http://dx.doi.org/10.1109/vizsec.2017.8062196.

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Ehlert, Sven, Ge Zhang, and Thomas Magedanz. "Increasing SIP firewall performance by ruleset size limitation." In 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE, 2008. http://dx.doi.org/10.1109/pimrc.2008.4699868.

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Lam, Ho-Yu, Donghan (Jarod) Wang, and H. Jonathan Chao. "A traffic-aware top-N firewall ruleset approximation algorithm." In the 6th ACM/IEEE Symposium. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1872007.1872019.

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Sasaki, Yoichi, and Yuzuru Okajima. "Alternative Ruleset Discovery to Support Black-box Model Predictions." In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 2021. http://dx.doi.org/10.1109/icdm51629.2021.00163.

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Scaperdas, Athena, Ian Lines, Marc Young, and Philip Pennicott. "QRA Ruleset for the Sour Pipelines of the Kashagan Field." In Abu Dhabi International Petroleum Exhibition & Conference. Society of Petroleum Engineers, 2017. http://dx.doi.org/10.2118/188884-ms.

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Reports on the topic "Ruleset"

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Letchford, Joshua, and Ruby Booth. Gaming Research for Alliance Network Dynamics ? Report 2 -- Assent Draft Ruleset. Office of Scientific and Technical Information (OSTI), February 2021. http://dx.doi.org/10.2172/1765623.

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Davies, K., and A. Freytag. Representing Label Generation Rulesets Using XML. RFC Editor, August 2016. http://dx.doi.org/10.17487/rfc7940.

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Freytag, A. Guidance on Designing Label Generation Rulesets (LGRs) Supporting Variant Labels. RFC Editor, August 2017. http://dx.doi.org/10.17487/rfc8228.

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Tillou, Michael, Supriya Goel, and Michael Rosenberg. Simplified Performance Rating Method - Review of Existing Tools, Rulesets, and Programs. Office of Scientific and Technical Information (OSTI), November 2020. http://dx.doi.org/10.2172/1814644.

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Cho, In-Koo, and James Bullard. Escapist Policy Rules. Federal Reserve Bank of St. Louis, 2002. http://dx.doi.org/10.20955/wp.2002.002.

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Machado, Dalmo, Barbara Christovam, Donizete Vago, and Marcia Gentil. Internal Rules OBJN. Fluminense Federal University, December 2017. http://dx.doi.org/10.17665/1676-4285.2018doc1.

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Rosenberg, J. Presence Authorization Rules. RFC Editor, December 2007. http://dx.doi.org/10.17487/rfc5025.

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Fimple, Stephen M. Rules - In a Knife Fight? A Study of Rules of Engagement. Fort Belvoir, VA: Defense Technical Information Center, February 2003. http://dx.doi.org/10.21236/ada415367.

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Gallmeyer, Michael, Burton Hollifield, and Stanley Zin. Taylor Rules, McCallum Rules and the Term Structure of Interest Rates. Cambridge, MA: National Bureau of Economic Research, April 2005. http://dx.doi.org/10.3386/w11276.

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Emmerson, Carl, Sarah Love, and Christine Frayne. The Government's fiscal rules. Institute for Fiscal Studies, April 2001. http://dx.doi.org/10.1920/bn.ifs.2001.0016.

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