Relevant bibliographies by topics / Discovery

Academic literature on the topic 'Discovery'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2024

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Journal articles on the topic "Discovery"

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Cook, D. J., and L. B. Holder. "Substructure Discovery Using Minimum Description Length and Background Knowledge." Journal of Artificial Intelligence Research 1 (February 1, 1994): 231–55. http://dx.doi.org/10.1613/jair.43.

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The ability to identify interesting and repetitive substructures is an essential component to discovering knowledge in structural data. We describe a new version of our SUBDUE substructure discovery system based on the minimum description length principle. The SUBDUE system discovers substructures that compress the original data and represent structural concepts in the data. By replacing previously-discovered substructures in the data, multiple passes of SUBDUE produce a hierarchical description of the structural regularities in the data. SUBDUE uses a computationally-bounded inexact graph match that identifies similar, but not identical, instances of a substructure and finds an approximate measure of closeness of two substructures when under computational constraints. In addition to the minimum description length principle, other background knowledge can be used by SUBDUE to guide the search towards more appropriate substructures. Experiments in a variety of domains demonstrate SUBDUE's ability to find substructures capable of compressing the original data and to discover structural concepts important to the domain. Description of Online Appendix: This is a compressed tar file containing the SUBDUE discovery system, written in C. The program accepts as input databases represented in graph form, and will output discovered substructures with their corresponding value.
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Zambrano, Diego. "Discovery as Regulation." Michigan Law Review, no. 119.1 (2020): 71. http://dx.doi.org/10.36644/mlr.119.1.discovery.

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This article develops an approach to discovery that is grounded in regulatory theory and administrative subpoena power. The conventional judicial and scholarly view about discovery is that it promotes fair and accurate outcomes and nudges the parties toward settlement. While commonly held, however, this belief is increasingly outdated and suffers from limitations. Among them, it has generated endless controversy about the problem of discovery costs. Indeed, a growing chorus of scholars and courts has offered an avalanche of reforms, from cost shifting and bespoke discovery contracts to outright elimination. Recently, Judge Thomas Hardiman quipped that if he had absolute power, he would abolish discovery for cases involving less than $500,000. These debates, however, are at a standstill, and existing scholarship offers incomplete treatment of discovery theory that might move debates forward. The core insight of the project is that in the private-enforcement context—where Congress deliberately employs private litigants as the main method of statutory enforcement—there is a surprisingly strong case that our current discovery system should be understood in part as serving regulatory goals analogous to administrative subpoena power. That is, discovery here can be seen as an extension of the subpoena power that agencies like the SEC, FTC, and EPA possess and is the lynchpin of a system that depends on private litigants to enforce our most important statutes. By forcing parties to disclose large amounts of information, discovery deters harm and, most importantly, shapes industry-wide practices and the primary behavior of regulated entities. This approach has a vast array of implications for the scope of discovery as well as the debate over costs. Scholars and courts should thus grapple with the consequences of what I call “regulatory discovery” for the entire legal system.
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Kamel, Mohammed B. M., Yuping Yan, Peter Ligeti, and Christoph Reich. "Attred: Attribute Based Resource Discovery for IoT." Sensors 21, no. 14 (July 10, 2021): 4721. http://dx.doi.org/10.3390/s21144721.

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While the number of devices connected together as the Internet of Things (IoT) is growing, the demand for an efficient and secure model of resource discovery in IoT is increasing. An efficient resource discovery model distributes the registration and discovery workload among many nodes and allow the resources to be discovered based on their attributes. In most cases this discovery ability should be restricted to a number of clients based on their attributes, otherwise, any client in the system can discover any registered resource. In a binary discovery policy, any client with the shared secret key can discover and decrypt the address data of a registered resource regardless of the attributes of the client. In this paper we propose Attred, a decentralized resource discovery model using the Region-based Distributed Hash Table (RDHT) that allows secure and location-aware discovery of the resources in IoT network. Using Attribute Based Encryption (ABE) and based on predefined discovery policies by the resources, Attred allows clients only by their inherent attributes, to discover the resources in the network. Attred distributes the workload of key generations and resource registration and reduces the risk of central authority management. In addition, some of the heavy computations in our proposed model can be securely distributed using secret sharing that allows a more efficient resource registration, without affecting the required security properties. The performance analysis results showed that the distributed computation can significantly reduce the computation cost while maintaining the functionality. The performance and security analysis results also showed that our model can efficiently provide the required security properties of discovery correctness, soundness, resource privacy and client privacy.
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Gagliardi, Francesco. "A Cognitive Approach to Scientific Data Mining for Syndrome Discovery." International Journal of Software Science and Computational Intelligence 4, no. 1 (January 2012): 1–33. http://dx.doi.org/10.4018/jssci.2012010101.

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The author introduces a machine learning system for cluster analysis to take on the problem of syndrome discovery in the clinical domain. A syndrome is a set of typical clinical features (a prototype) that appear together often enough to suggest they may represent a single, unknown, disease. The discovery of syndromes and relative taxonomy formation is therefore the critical early phase of the process of scientific discovery in the medical domain. The system proposed discovers syndromes following Eleanor Rosch’s prototype theory on how the human mind categorizes and forms taxonomies, and thereby to understand how humans perform these activities and to automate or assist the process of scientific discovery. The system implemented can be considered a scientific discovery support system as it can discover unknown syndromes to the advantage of subsequent clinical practices and research activities.
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Ramadhan, Haider Ali. "DISCOVER: AN INTELLIGENT DISCOVERY PROGRAMMING SYSTEM." Cybernetics and Systems 31, no. 1 (January 2000): 87–114. http://dx.doi.org/10.1080/019697200124937.

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Das, Shubhomoy, Md Rakibul Islam, Nitthilan Kannappan Jayakodi, and Janardhan Rao Doppa. "Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning." Journal of Artificial Intelligence Research 80 (May 23, 2024): 127–70. http://dx.doi.org/10.1613/jair.1.14741.

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Anomaly detection (AD) task corresponds to identifying the true anomalies among a given set of data instances. AD algorithms score the data instances and produce a ranked list of candidate anomalies. The ranked list of anomalies is then analyzed by a human to discover the true anomalies. Ensemble of tree-based anomaly detectors trained in an unsupervised manner and scoring based on uniform weights for ensembles are shown to work well in practice. However, the manual process of analysis can be laborious for the human analyst when the number of false-positives is very high. Therefore, in many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. One important way to configure the detector is by providing true labels (nominal or anomaly) for a few instances. Recent work on active anomaly discovery has shown that greedily querying the top-scoring instance and tuning the weights of ensembles based on label feedback allows us to quickly discover true anomalies. This paper makes four main contributions to improve the state-of-the-art in anomaly discovery using tree-based ensembles. First, we provide an important insight that explains the practical successes of unsupervised tree-based ensembles and active learning based on greedy query selection strategy. We also show empirical results on real-world data to support our insights and theoretical analysis to support active learning. Second, we develop a novel batch active learning algorithm to improve the diversity of discovered anomalies based on a formalism called compact description to describe the discovered anomalies. Third, we develop a novel active learning algorithm to handle streaming data setting. We present a data drift detection algorithm that not only detects the drift robustly, but also allows us to take corrective actions to adapt the anomaly detector in a principled manner. Fourth, we present extensive experiments to evaluate our insights and our tree-based active anomaly discovery algorithms in both batch and streaming data settings. Our results show that active learning allows us to discover significantly more anomalies than state-of-the-art unsupervised baselines, our batch active learning algorithm discovers diverse anomalies, and our algorithms under the streaming-data setup are competitive with the batch setup.
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KANWAL, ATTIYA, SAHAR FAZAL, SOHAIL ASGHAR, and Muhammad Naeem. "SUBGROUP DISCOVERY OF THE MODY GENES;." Professional Medical Journal 20, no. 05 (October 15, 2013): 644–52. http://dx.doi.org/10.29309/tpmj/2013.20.05.1207.

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Background: The pandemic of metabolic disorders is accelerating in the urbanized world posing huge burden to healthand economy. The key pioneer to most of the metabolic disorders is Diabetes Mellitus. A newly discovered form of diabetes is MaturityOnset Diabetes of the Young (MODY). MODY is a monogenic form of diabetes. It is inherited as autosomal dominant disorder. Till to date11 different MODY genes have been reported. Objective: This study aims to discover subgroups from the biological text documentsrelated to these genes in public domain database. Data Source: The data set was obtained from PubMed. Period: September-December,2011. Materials and Methodology: APRIORI-SD subgroup discovery algorithm is used for the task of discovering subgroups. A wellknown association rule learning algorithm APRIORI is first modified into classification rule learning algorithm APRIORI-C. APRIORI-Calgorithm generates the rule from the discretized dataset with the minimum support set to 0.42% with no confidence threshold. Total 580rules are generated at the given support. APRIOIR-C is further modified by making adaptation into APRIORI-SD. Results: Experimentalresults demonstrate that APRIORI discovers the substantially smaller rule sets; each rule has higher support and significance. The rulesthat are obtained by APRIORI-C are ordered by weighted relative accuracy. Conclusion: Only first 66 rules are ordered as they cover therelation between all the 11 MODY genes with each other. These 66 rules are further organized into 11 different subgroups. The evaluationof obtained results from literature shows that APRIORI-SD is a competitive subgroup discovery algorithm. All the association amonggenes proved to be true.
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Romanova, V. V. "DISCOVERY AND DEVELOPMENT OF IULTINSKOYE TIN-TUNGSTEN DEPOSIT." Proceedings of higher educational establishments. Geology and Exploration, no. 3 (June 25, 2018): 65–73. http://dx.doi.org/10.32454/0016-7762-2018-3-65-73.

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The Iultinskoye deposit was discovered 80 years ago in the remote and unexplored region of Chukotka. Discovery and development of the deposit were accompanied by serious difficulties. A brief biographic data about the discoverer of the deposit V.N. Milyaev has been presented here. The Iultinsky mining processing plant, which was opened in 1959, was closed in 1994 due to the changing in the economic situation. The deposit was conserved, Iultinsky village was liquidated. Vernadsky State Geological Museum RAS has samples of cassiterite and wolframite, collected by geologists of the Second Chukotka Glavsevmorput expedition in the year of the deposit discovery.
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Cai, Borui, Guangyan Huang, Yong Xiang, Maia Angelova, Limin Guo, and Chi-Hung Chi. "Multi-Scale Shapelets Discovery for Time-Series Classification." International Journal of Information Technology & Decision Making 19, no. 03 (May 2020): 721–39. http://dx.doi.org/10.1142/s0219622020500133.

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Shapelets are subsequences of time-series that represent local patterns and can improve the accuracy and the interpretability of time-series classification. The major task of time-series classification using shapelets is to discover high quality shapelets. However, this is challenging since local patterns may have various scales/lengths rather than a unified scale. In this paper, we resolve this problem by discovering shapelets with multiple scales. We propose a novel Multi-Scale Shapelet Discovery (MSSD) algorithm to discover expressive multi-scale shapelets by extending initial single-scale shapelets (i.e., shapelets with a unified scale). MSSD adopts a bi-directional extension process and is robust to extend single-shapelets obtained by different methods. A supervised shapelet quality measurement is further developed to qualify the extension of shapelets. Comprehensive experiments conducted on 25 UCR time-series datasets show that multi-scale shapelets discovered by MSSD improve classification accuracy by around 10% (in average), compared with single-scale shapelets discovered by counterpart methods.
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Sit, Masganti, Putri Lestari, and Yusnaili Budianti. "Improving The Understanding of Science Concept Through Guided Discovery Learning Model in Azzahra Preschool Kindergarten." Unnes Science Education Journal 9, no. 3 (November 21, 2020): 128–36. http://dx.doi.org/10.15294/usej.v9i3.39590.

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Understanding simple concepts (scientific concepts) was one of important understanding in aspects of cognitive development for early childhood. Children were trained to think actively and critically in order to understand they activities, namely by applying a constructivist approach or guided discovey learning. However in Azzahra Preschool Kindergarten has not applied a guided discovey learning. It considered to be a factor in children’s low understanding of science concepts. Therefore, this research aimed to improve the children's understanding of science concepts by applying guided discovery learning. This research used a classroom action research with two cycles. Subject of this research is all of children group B in Azzahra Preschool Kindergarten. And the results show that the guided discovery learning can improved children’s understanding of science concept. This is based on percentage score of children’s understanding of science concept which is increasing in each cycle. Other findings of this research showed that children were eager to learn, curiosity was increasing, and active to conduct experiments to discover various simple concepts. So, this research was recommended that apply the guided discovery learning model to develop various aspects of the child.
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Dissertations / Theses on the topic "Discovery"

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Keck, Andrew G. "Electronic discovery." Thesis, Utica College, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10101099.

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Cyber incidents continue to increase across the entire globe. The increase in security threats requires organizations to rethink strategies and policies continually fortifying against known and unknown threats. Cyber incident policies and response plans range from non-existent to hundreds of pages in length. A policy may include sections discussing roles and responsibility, incident detection, escalation, and many additional categories, and often discuss the collection and preservation of forensic evidence. Policies briefly address, in many cases, the proper collection of evidence; however, the written regulation concerning the potential liabilities, the risks associated with current and future litigation, and the legal consequences to a cyber incident remains sparse. The desired outcome of this paper is to enlighten the reader through identification of the risks, the potential pitfalls, and steps to policy development pertaining to the handling of electronic evidence, with a cross examination of overlapping sectors between forensics, electronic discovery, and cyber security.

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Firriolo, Marco. "Discovery copy." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8224/.

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McCoy, Jan. "Outdoor Discovery." College of Agriculture, University of Arizona (Tucson, AZ), 1989. http://hdl.handle.net/10150/295610.

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Wendel, Patrick. "The architecture of discovery net : towards grid-based discovery services." Thesis, Imperial College London, 2008. http://hdl.handle.net/10044/1/7708.

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Hildebrandt, Leonore S. "A Small Discovery." Fogler Library, University of Maine, 2004. http://www.library.umaine.edu/theses/pdf/HildebrandtLS2004.pdf.

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Cheng, Peter C.-H. "Modelling scientific discovery." Thesis, Open University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.256257.

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Eriksson, Gustav, and Martin Kevin Garcia. "Discovery of Neptune." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230700.

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This project is an analysis of how a planet can be found in space with the aid of mathematics. This is based on the fact that in the 19th century two mathematicians John C. Adams and Urbain Le Verrier both independent of each other found Neptune, the 8th planet in the solar system, by calculating its location based on discrepancies between theoretical and observed longitudes. We recreate Adams’ problem and solve it with numerical analysis to see how one could improve this method of finding a planet using mathematics. We created a model of the solar system using Runge-Kutta 4 (RK4) to solve ODE’s explaining how the planets affect each other. We then created an inverse problem where we pretended that Neptune did not exist and tried to find its position and data using Gauss-Newton’s algorithm. Our method gives a better result than those of Adams, although we use a better start guess for the position of Neptune than he did. The important parameter to find is at what direction to look for the planet, also called the longitude angle. Both Adams and us get close to the correct longitude—Adams’ being 2:5_ off and us within 1_. This is especially interesting since without getting this parameter correct they would never have found the planet at that time.
Detta projekt är en analys om hur en planet kan hittas i rymden med hjälp av matematik. Det är baserat på två matematiker, John C. Adams och Urbain Le Verrier, som på 1800-talet oberoende av varandra hittade Neptunus, den åttonde planeten i solsystemet, genom att approximera dess position baserat på avvikelser mellan teoretiska och observerade longituder. Vi återskapar Adams problem och löser det med numerisk analys för att se hur man kan förbättra metoden att hitta planeter genom matematik. Vi skapade en modell av solsystemet med Runge-Kutta 4 (RK4) för att lösa ODE’s som beskriver hur planeterna påverkar varandra. Sedan skapar vi ett inverterat problem där vi låtsas om att Neptunus inte finns och försöker hitta dess position med Gauss-Newtons algoritm. Vår metod ger ett bättre resultat än Adams, vilket beror på att vi använder en bättre startgissning för Neptunus position. Den viktiga parametern att hitta är vid vilken vinkel man ska kolla efter planeten, även kallat longitudvinkeln. Både Adams och vi kommer nära det riktiga värdet --Adams är 2,5  ifrån och vi är inom 1. Detta är särskilt intressant eftersom de aldrig skulle hittat planeten utan denna parameter.
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Taylor, Jonathan Lorin. "Lines of Discovery." Thesis, Virginia Tech, 2004. http://hdl.handle.net/10919/35461.

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An entry for the World Trade Center Memorial Competition was expanded upon as a study into the nature of design. The project influenced its own evolution and revealed exciting outcomes. The memorial is a reinforced concrete tower with an acrylic water tank at the top. The water tank acts as a prism casting colorful light displays both in the tower and around the site. The tank is also the source for continually falling droplets of water. The drops fall 450 feet through an open chamber to land in a shallow overflowing pool and then wash over a stone sepulcher containing the unidentified remains of victims.
Master of Architecture
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Oliveira, Olga Margarida Fajarda. "Network topology discovery." Doctoral thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/18692.

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Doutoramento em Matemática
A monitorização e avaliação do desempenho de uma rede são essenciais para detetar e resolver falhas no seu funcionamento. De modo a conseguir efetuar essa monitorização, e essencial conhecer a topologia da rede, que muitas vezes e desconhecida. Muitas das técnicas usadas para a descoberta da topologia requerem a cooperação de todos os dispositivos de rede, o que devido a questões e políticas de segurança e quase impossível de acontecer. Torna-se assim necessário utilizar técnicas que recolham, passivamente e sem a cooperação de dispositivos intermédios, informação que permita a inferência da topologia da rede. Isto pode ser feito recorrendo a técnicas de tomografia, que usam medições extremo-a-extremo, tais como o atraso sofrido pelos pacotes. Nesta tese usamos métodos de programação linear inteira para resolver o problema de inferir uma topologia de rede usando apenas medições extremo-a-extremo. Apresentamos duas formulações compactas de programação linear inteira mista (MILP) para resolver o problema. Resultados computacionais mostraram que a medida que o número de dispositivos terminais cresce, o tempo que as duas formulações MILP compactas necessitam para resolver o problema, também cresce rapidamente. Consequentemente, elaborámos duas heurísticas com base nos métodos Feasibility Pump e Local ranching. Uma vez que as medidas de atraso têm erros associados, desenvolvemos duas abordagens robustas, um para controlar o número máximo de desvios e outra para reduzir o risco de custo alto. Criámos ainda um sistema que mede os atrasos de pacotes entre computadores de uma rede e apresenta a topologia dessa rede.
Monitoring and evaluating the performance of a network is essential to detect and resolve network failures. In order to achieve this monitoring level, it is essential to know the topology of the network which is often unknown. Many of the techniques used to discover the topology require the cooperation of all network devices, which is almost impossible due to security and policy issues. It is therefore, necessary to use techniques that collect, passively and without the cooperation of intermediate devices, the necessary information to allow the inference of the network topology. This can be done using tomography techniques, which use end-to-end measurements, such as the packet delays. In this thesis, we used some integer linear programming theory and methods to solve the problem of inferring a network topology using only end-to-end measurements. We present two compact mixed integer linear programming (MILP) formulations to solve the problem. Computational results showed that as the number of end-devices grows, the time need by the two compact MILP formulations to solve the problem also grows rapidly. Therefore, we elaborate two heuristics based on the Feasibility Pump and Local Branching method. Since the packet delay measurements have some errors associated, we developed two robust approaches, one to control the maximum number of deviations and the other to reduce the risk of high cost. We also created a system that measures the packet delays between computers on a network and displays the topology of that network.
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Seneviratne, Dilesha Nilakshi. "Patent link discovery." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/131137/1/Dilesha%20Nilakshi%20Seneviratne_Dissanayake%20Wasala%20Mudiyanselage%20Hakmana%20Walawwe_Thesis.pdf.

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Patents contain useful technical information about inventions; however, accessing the knowledge in patents requires considerable effort, which results in an under-usage of patent knowledge. This research investigated the core techniques that are required to create hyperlinks for patents similar to the hyperlinks in the Wikipedia. Such a system can make knowledge captured in patents more accessible to a wide range of patent users including scientific communities. In doing so, a new context matching technique was proposed to improve the accuracy of linking. In general, the research has extended the understanding of linking methods in the patent domain.
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Books on the topic "Discovery"

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Tenney, Charles D. The discovery of discovery. Lanham: University Press of America, 1991.

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Allcorn, S. Robert. Discovery. 2nd ed. [St. Paul, Minn.]: Thomson/West, 2006.

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J, Twieg Bonnie, ed. Discovery. [San Francisco?]: Bancroft-Whitney, 1996.

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Allcorn, S. Robert. Discovery. St. Paul, Minn: West Group, 1998.

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M, Lauriat Peter, ed. Discovery. 2nd ed. [St. Paul, Minn.]: Thomson/West, 2008.

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Carlton, Larry. Discovery. Universal City, Calif: MCA Records, 1987.

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M, Lauriat Peter, ed. Discovery. [St. Paul, Minn.]: West Group, 2001.

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National Air and Space Museum, ed. Discovery. [Washington, D.C.?]: National Aeronautics and Space Administration, 1988.

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Hoving, Thomas. Discovery. New York: Simon and Schuster, 1989.

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Labella, Zenaida. Self-Discovery: Discover a Journey of Self-Discovery. Independently Published, 2022.

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Book chapters on the topic "Discovery"

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McNeely, Connie L. "Discovery Analytics, Discovery Informatics." In Encyclopedia of Big Data, 1–2. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-32001-4_74-2.

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McNeely, Connie L. "Discovery Analytics, Discovery Informatics." In Encyclopedia of Big Data, 403–4. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-32010-6_74.

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McNeely, Connie L. "Discovery Analytics, Discovery Informatics." In Encyclopedia of Big Data, 1–2. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-32001-4_74-1.

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Gorman, Michael E. "Discovery." In Transforming Nature, 1–50. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5657-2_1.

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Dux, Moira C. "Discovery." In Encyclopedia of Clinical Neuropsychology, 1185–86. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_969.

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Hall, Brian K. "Discovery." In The Neural Crest in Development and Evolution, 3–13. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4757-3064-7_1.

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Rubinfeld, Daniel L. "Discovery." In The New Palgrave Dictionary of Economics and the Law, 609–15. London: Palgrave Macmillan UK, 2002. http://dx.doi.org/10.1007/978-1-349-74173-1_116.

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Dustdar, Schahram, Christian Platzer, and Bernd J. Krämer. "Discovery." In Encyclopedia of Database Systems, 1–6. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_1177-2.

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Dux, Moira C. "Discovery." In Encyclopedia of Clinical Neuropsychology, 1. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56782-2_969-2.

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Smith, R. B. "Discovery." In The Development of a Medicine, 9–19. London: Macmillan Education UK, 1985. http://dx.doi.org/10.1007/978-1-349-17954-1_2.

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Conference papers on the topic "Discovery"

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Zhang, Sheng, Ryan Jacobs, Sayan Ghosh, Ambarish Kulkarni, and Liping Wang. "Automated Data-Driven Physics Discovery of Turbine Component Damage." In ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-83372.

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Abstract We propose an automated physics discovery algorithm for turbine component damage modeling. Our algorithm utilizes operational data of a mechanical component and discovers an interpretable symbolic formula that describes the physics. We illustrate our algorithm through two numerical examples and demonstrate that the discovered formulas can predict the future damage accurately. Our framework is flexible and easily applicable to all areas of science and engineering. With cutting-edge machine learning tools, researchers can simply input the experimental data and then the physics formulas are printed out automatically. The application of this new algorithm may reduce the time spent on research and development of physics models significantly, while achieving almost the best accuracy in prediction.
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Beck, Vicki Dobbs. "Discovery channel - super discovery." In ACM SIGGRAPH 98 Electronic art and animation catalog. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/281388.281855.

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Luan, Lele, Ryan Jacobs, Sayan Ghosh, and Liping Wang. "Physics Discovery of Engineering Applications With Constrained Optimization and Genetic Programming." In ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/gt2023-104043.

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Abstract Discovering physics from data have the potential to advance our understanding and prediction of a system where the governing physics are unknown but experimental data are available. Physics-based models are conventionally obtained from rigorous first principles derivations such as conservation laws or knowledge-based phenomenological derivations. However, inferring physics-based models remains challenging for many real-world complex systems with intertwined and coupled multi-physics that are challenging to derive from first principles. In recent years, data-driven physics discovery has become an attractive research topic across many scientific disciplines due to the increasing richness of observational data and advances in machine learning. Various data-driven physics discovery algorithms have been proposed to extract simple but appropriate models governing the systems from the observed data. Among the proposed algorithms, genetic programming (GP) based symbolic regression (SR) plays a dominant role. Here, we build a general GP based SR platform to discover physical models from observations for real-world systems. The platform is used to solve an optimization problem where the outcome is a set of human-readable equations that the user can select from based on accuracy vs parsimony trades. The discovery process also considers the prior knowledge of experts, which manifests as constraints to enhance the embedding of physics and avoid overfitting. The discovered models not only have high interpretability but also demonstrate good extrapolation performance.
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A. Kieliszewski, Cheryl, Laura Challman Anderson, and Susan U. Stucky. "A Case Study: Designing the Service Experience for Big Data Discovery." In Applied Human Factors and Ergonomics Conference. AHFE International, 2019. http://dx.doi.org/10.54941/ahfe100287.

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The purpose of this paper is to present early-stage results from three investigations we have undertaken to support the goal of collaborative innovation to scale discovery. This work is being conducted at IBM Research - Almaden in the recently announced Accelerated Discovery Laboratory. The motivation for this work was the need to experiment and investigate ideas of how to accelerate discovery in a real-world setting (at the very same time that we were learning what it means to “discover”). The underlying tenets borrow ideas from Service Science to build a framework for discovery in which the Participant’s service experience takes place across and within technical, social, and spatial systems. In the big picture view, our goal is to be able to capture project teams’ journeys through this framework and provide navigational assistance. We present results of the investigations to start identifying the resource and co-creative patterns of Participants – examining what it means to “discover” and perform work in a living lab, factors that impact information sharing by heterogeneous teams, and mapping of the service experience to establish a shared mental model.
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Trzcielinski, Stefan. "Human intelligence vs Artificial intelligence in opportunity discovery." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003512.

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Opportunity is a core phenomenon in theories of: strategic management, entrepreneurship and agile enterprise. Usually it is meant as an external situation which favours the enterprise achieving its objectives and goals with accessible resources. Strategists and entrepreneurs search for opportunities to improve the firm’s performance. Some of them do this intuitively when others apply methods of strategic analysis and/or conduct marketing research. The Artificial intelligence particular cognitive computing creates new possibilities to discover opportunities. The aim of this paper is to present a general model of opportunity recognition that applies to all above mentioned approaches. Basing on this model a review of traditional methods of opportunity discovery, exploiting mostly human intelligence, is presented. Next a generic model of Artificial Intelligence aided opportunity discovery is discussed. The article also shows the results of a study of companies' use of traditional opportunity discovery methods and preliminary results of a study of opportunity discovery using artificial intelligence.
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Jones, Allen, Cynthia R. Schwarz, Hannah McKelvey, Rachelle McLain, and Christine Stohn. "Resource Discovery in a Changing Content World." In Charleston Library Conference. Purdue Univeristy, 2020. http://dx.doi.org/10.5703/1288284317157.

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Discovery services have evolved to include not just books and articles, but databases, website content, research guides, digital and audiovisual collections, and unique local collections that are all important for their users to be able to find. Search and ranking remain at the core of discovery, but advanced tools such as recommendation, virtual browse, ‘look inside‘, and the use of artificial intelligence are also becoming more prevalent. This group of panelists discussed how content in their discovery systems can change based on the context of the user, using as examples Primo and Blacklight, and how content is populated, discovered and requested by users through differing customizations and workflows. The session also explored what tools are available today or may become available in the coming years that may be used to highlight different collections and material types in a library discovery system. As this topic impacts many stakeholders—libraries who need to make content discoverable and satisfy the needs of their users, content providers who want to make sure that their content is visible and used, and discovery providers who need to develop their systems to support the changing needs—the panelists posed questions to the audience to encourage conversation around the challenges they face with making their unique content collections discoverable and to share solutions.
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"Process Discovery - Automated Approach for Block Discovery." In 9th International Conference on Evaluation of Novel Software Approaches to Software Engineering. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004896402040211.

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Wan, Mengting, Xiangyu Chen, Lance Kaplan, Jiawei Han, Jing Gao, and Bo Zhao. "From Truth Discovery to Trustworthy Opinion Discovery." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2939672.2939837.

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Wadduwage, Dushan N. "Stochastic Proof by Contradiction Complements Machine-learning-driven Biomarker Discovery in Image Data." In Clinical and Translational Biophotonics. Washington, D.C.: Optica Publishing Group, 2024. http://dx.doi.org/10.1364/translational.2024.jm4a.55.

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Given image data, machine-learning-based classifiers “discover” biomarkers under the hypothesis they exist; This is suspectable to confirmation bias. Instead, we propose an approach to disprove the null hypothesis of non-existence, countering bias in biomarker discovery.
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Wang, Hao, Yukio Ohsawa, Yoko Nishihara, and Lin Guo. "An effective method to discover emerging chance in chance discovery." In 2010 9th IEEE International Conference on Cognitive Informatics (ICCI). IEEE, 2010. http://dx.doi.org/10.1109/coginf.2010.5599832.

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Reports on the topic "Discovery"

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Duffie, Darrell, and Haoxiang Zhu. Size Discovery. Cambridge, MA: National Bureau of Economic Research, November 2015. http://dx.doi.org/10.3386/w21696.

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Cheshire, S. Discovery Proxy for Multicast DNS-Based Service Discovery. RFC Editor, June 2020. http://dx.doi.org/10.17487/rfc8766.

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Conta, A. Extensions to IPv6 Neighbor Discovery for Inverse Discovery Specification. RFC Editor, June 2001. http://dx.doi.org/10.17487/rfc3122.

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Maenpaa, J., and G. Camarillo. Service Discovery Usage for REsource LOcation And Discovery (RELOAD). RFC Editor, October 2014. http://dx.doi.org/10.17487/rfc7374.

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Green, L. The Klondike discovery. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1996. http://dx.doi.org/10.4095/298502.

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Mogul, J. C., and S. E. Deering. Path MTU discovery. RFC Editor, November 1990. http://dx.doi.org/10.17487/rfc1191.

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Haberman, B., and J. Martin. Multicast Router Discovery. RFC Editor, December 2005. http://dx.doi.org/10.17487/rfc4286.

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Fox, Andrew, and Sadie Walters. Charlotte Brody Discovery Garden. Landscape Architecture Foundation, 2015. http://dx.doi.org/10.31353/cs0920.

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Loehle, C. Discovery as a process. Office of Scientific and Technical Information (OSTI), May 1994. http://dx.doi.org/10.2172/10144535.

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LOY, DOUGLAS A., ELI E. SOTO, and DAVID R. WHEELER. Rapid Discovery of Materials. Office of Scientific and Technical Information (OSTI), October 2001. http://dx.doi.org/10.2172/787795.

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