Literatura académica sobre el tema "Sensitive date"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Sensitive date".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Sensitive date"
Culp, Rex E. "Up-to-Date, but Are They Culturally Sensitive?" Contemporary Psychology: A Journal of Reviews 37, n.º 9 (septiembre de 1992): 899. http://dx.doi.org/10.1037/032570.
Texto completoKovi, Mallikarjuna Rao, Yong Hu, Xufeng Bai y Yongzhong Xing. "QTL mapping for thermo-sensitive heading date in rice". Euphytica 205, n.º 1 (8 de febrero de 2015): 51–62. http://dx.doi.org/10.1007/s10681-015-1383-6.
Texto completoFang, Jinggui y ChihCheng T. Chao. "Methylation Sensitive Amplification Polymorphism in Date Palms and Their Offshoots". HortScience 41, n.º 4 (julio de 2006): 994A—994. http://dx.doi.org/10.21273/hortsci.41.4.994a.
Texto completoCRANSTON, PETER S., FRANK-THORSTEN KRELL, KEN WALKER y DAVID HEWES. "Wiley's Early View constitutes valid publication for date-sensitive nomenclature". Systematic Entomology 40, n.º 1 (enero de 2015): 2–4. http://dx.doi.org/10.1111/syen.12119.
Texto completoAbdulai, A. L., M. Kouressy, M. Vaksmann, F. Asch, M. Giese y B. Holger. "Latitude and Date of Sowing Influences Phenology of Photoperiod-Sensitive Sorghums". Journal of Agronomy and Crop Science 198, n.º 5 (19 de junio de 2012): 340–48. http://dx.doi.org/10.1111/j.1439-037x.2012.00523.x.
Texto completoZiouti, A., C. Modafar, A. Fleuriet, S. Boustani y J. J. Macheix. "Phenolic compounds in date palm cultivars sensitive and resistant toFusarium oxysporum". Biologia plantarum 38, n.º 3 (1 de septiembre de 1996): 451–57. http://dx.doi.org/10.1007/bf02896679.
Texto completoTownsend, Teresa, Leigh Lane y James Martin. "Context-Sensitive Solutions". Transportation Research Record: Journal of the Transportation Research Board 1904, n.º 1 (enero de 2005): 66–74. http://dx.doi.org/10.1177/0361198105190400107.
Texto completoPetrides, George y Wouter Verbeke. "Cost-sensitive ensemble learning: a unifying framework". Data Mining and Knowledge Discovery 36, n.º 1 (28 de septiembre de 2021): 1–28. http://dx.doi.org/10.1007/s10618-021-00790-4.
Texto completoCollinson, Paul O. "Sensitive troponin assays". Journal of Clinical Pathology 64, n.º 10 (24 de junio de 2011): 845–49. http://dx.doi.org/10.1136/jclinpath-2011-200164.
Texto completoWang, Yulu, Fei Liu, Yuemeng Yang y Li-Ping Xu. "Droplet evaporation-induced analyte concentration toward sensitive biosensing". Materials Chemistry Frontiers 5, n.º 15 (2021): 5639–52. http://dx.doi.org/10.1039/d1qm00500f.
Texto completoTesis sobre el tema "Sensitive date"
Ema, Ismat. "Sensitive Data Migration to the Cloud". Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-64736.
Texto completoFolkesson, Carl. "Anonymization of directory-structured sensitive data". Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160952.
Texto completoSubbiah, Arun. "Efficient Proactive Security for Sensitive Data Storage". Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19719.
Texto completoBakri, Mustafa al. "Uncertainty-Sensitive Reasoning over the Web of Data". Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENM073.
Texto completoIn this thesis we investigate several approaches that help users to find useful and trustful informationin the Web of Data using the Semantic Web technologies. In this purpose, we tackle tworesearch issues: Data Linkage in Linked Data and Trust in Semantic P2P Networks. We model the problem of data linkage in Linked Data as a reasoning problem on possibly decentralized data. We describe a novel Import-by-Query algorithm that alternates steps of subquery rewriting and of tailored querying the Linked Data cloud in order to import data as specific as possible for inferring or contradicting given target same-as facts. Experiments conducted on real-world datasets have demonstrated the feasibility of this approach and its usefulness in practice for data linkage and disambiguation. Furthermore, we propose an adaptation of this approach to take into account possibly uncertain data and knowledge, with a result the inference of same-as and different-from links associated with probabilistic weights. In this adaptation we model uncertainty as probability values. Our experiments have shown that our adapted approach scales to large data sets and produces meaningful probabilistic weights. Concerning trust, we introduce a trust mechanism for guiding the query-answering process in Semantic P2P Networks. Peers in Semantic P2P Networks organize their information using separate ontologies and rely on alignments between their ontologies for translating queries. Trust is such a setting is subjective and estimates the probability that a peer will provide satisfactory answers for specific queries in future interactions. In order to compute trust, the mechanism exploits the information provided by alignments, along with the one that comes from peer's experiences. The calculated trust values are refined over time using Bayesian inference as more queries are sent and answers received. For the evaluation of our mechanism, we have built a semantic P2P bookmarking system (TrustMe) in which we can vary different quantitative and qualitative parameters. The experimental results show the convergence of trust, and highlight the gain in the quality of peers' answers —measured with precision and recall— when the process of query answering is guided by our trust mechanism
Ljus, Simon. "Purging Sensitive Data in Logs Using Machine Learning". Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-411610.
Texto completoDetta examensarbete undersöker om det är möjligt att skapa ett program som automatiskt identifierar och tar bort persondata från dataloggar med hjälp av maskinlärning. Att förstå innebörden av vissa ord kräver också kontext: Banan kan syfta på en banan som man kan äta eller en bana som man kan springa på. Kan en maskinlärningsmodell ta nytta av föregående och efterkommande ord i en sekvens av ord för att få en bättre noggrannhet på om ordet är känsligt eller ej. Typen av data som förekommer i loggarna kan vara bland annat namn, personnummer, användarnamn och epostadress. För att modellen ska kunna lära sig att känna igen datan krävs det att det finns data som är färdigannoterad med facit i hand. Telefonnummer, personnummer och epostadress kan bara se ut på ett visst sätt och behöver nödvändigtvis ingen maskininlärning för att kunna pekas ut. Kan man skapa en generell modell som fungerar på flera typer av dataloggar utan att använda regelbaserade algoritmer. Resultaten visar att den annoterade datan som användes för träning kan ha skiljt allt för mycket från de loggar som har testats på (osedd data), vilket betyder att modellen inte är bra på att generalisera.
Oshima, Sonoko. "Neuromelanin‐Sensitive Magnetic Resonance Imaging Using DANTE Pulse". Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263531.
Texto completoEl-Khoury, Hiba. "Introduction of New Products in the Supply Chain : Optimization and Management of Risks". Thesis, Jouy-en Josas, HEC, 2012. http://www.theses.fr/2012EHEC0001/document.
Texto completoShorter product life cycles and rapid product obsolescence provide increasing incentives to introduce newproducts to markets more quickly. As a consequence of rapidly changing market conditions, firms focus onimproving their new product development processes to reap the benefits of early market entry. Researchershave analyzed market entry, but have seldom provided quantitative approaches for the product rolloverproblem. This research builds upon the literature by using established optimization methods to examine howfirms can minimize their net loss during the rollover process. Specifically, our work explicitly optimizes thetiming of removal of old products and introduction of new products, the optimal strategy, and the magnitudeof net losses when the market entry approval date of a new product is unknown. In the first paper, we use theconditional value at risk to optimize the net loss and investigate the effect of risk perception of the manageron the rollover process. We compare it to the minimization of the classical expected net loss. We deriveconditions for optimality and unique closed-form solutions for single and dual rollover cases. In the secondpaper, we investigate the rollover problem, but for a time-dependent demand rate for the second producttrying to approximate the Bass Model. Finally, in the third paper, we apply the data-driven optimizationapproach to the product rollover problem where the probability distribution of the approval date is unknown.We rather have historical observations of approval dates. We develop the optimal times of rollover and showthe superiority of the data-driven method over the conditional value at risk in case where it is difficult to guessthe real probability distribution
Gholami, Ali. "Security and Privacy of Sensitive Data in Cloud Computing". Doctoral thesis, KTH, Parallelldatorcentrum, PDC, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186141.
Texto completo“Cloud computing”, eller “molntjänster” som blivit den vanligaste svenska översättningen, har stor potential. Molntjänster kan tillhandahålla exaktden datakraft som efterfrågas, nästan oavsett hur stor den är; dvs. molntjäns-ter möjliggör vad som brukar kallas för “elastic computing”. Effekterna avmolntjänster är revolutionerande inom många områden av datoranvändning.Jämfört med tidigare metoder för databehandling ger molntjänster mångafördelar; exempelvis tillgänglighet av automatiserade verktyg för att monte-ra, ansluta, konfigurera och re-konfigurera virtuella resurser “allt efter behov”(“on-demand”). Molntjänster gör det med andra ord mycket lättare för or-ganisationer att uppfylla sina målsättningar. Men det paradigmskifte, sominförandet av molntjänster innebär, skapar även säkerhetsproblem och förutsätter noggranna integritetsbedömningar. Hur bevaras det ömsesidiga förtro-endet, hur hanteras ansvarsutkrävandet, vid minskade kontrollmöjligheter tillföljd av delad information? Följaktligen behövs molnplattformar som är såkonstruerade att de kan hantera känslig information. Det krävs tekniska ochorganisatoriska hinder för att minimera risken för dataintrång, dataintrångsom kan resultera i enormt kostsamma skador såväl ekonomiskt som policymässigt. Molntjänster kan innehålla känslig information från många olikaområden och domäner. Hälsodata är ett typiskt exempel på sådan information. Det är uppenbart att de flesta människor vill att data relaterade tillderas hälsa ska vara skyddad. Så den ökade användningen av molntjänster påsenare år har medfört att kraven på integritets- och dataskydd har skärptsför att skydda individer mot övervakning och dataintrång. Exempel på skyd-dande lagstiftning är “EU Data Protection Directive” (DPD) och “US HealthInsurance Portability and Accountability Act” (HIPAA), vilka båda kräverskydd av privatlivet och bevarandet av integritet vid hantering av informa-tion som kan identifiera individer. Det har gjorts stora insatser för att utvecklafler mekanismer för att öka dataintegriteten och därmed göra molntjänsternasäkrare. Exempel på detta är; kryptering, “trusted platform modules”, säker“multi-party computing”, homomorfisk kryptering, anonymisering, container-och “sandlåde”-tekniker.Men hur man korrekt ska skapa användbara, integritetsbevarande moln-tjänster för helt säker behandling av känsliga data är fortfarande i väsentligaavseenden ett olöst problem på grund av två stora forskningsutmaningar. Fördet första: Existerande integritets- och dataskydds-lagar kräver transparensoch noggrann granskning av dataanvändningen. För det andra: Bristande kän-nedom om en rad kommande och redan existerande säkerhetslösningar för att skapa effektiva molntjänster.Denna avhandling fokuserar på utformning och utveckling av system ochmetoder för att hantera känsliga data i molntjänster på lämpligaste sätt.Målet med de framlagda lösningarna är att svara de integritetskrav som ställsi redan gällande lagstiftning, som har som uttalad målsättning att skyddaindividers integritet vid användning av molntjänster.Vi börjar med att ge en överblick av de viktigaste begreppen i molntjäns-ter, för att därefter identifiera problem som behöver lösas för säker databe-handling vid användning av molntjänster. Avhandlingen fortsätter sedan med en beskrivning av bakgrundsmaterial och en sammanfattning av befintligasäkerhets- och integritets-lösningar inom molntjänster.Vårt främsta bidrag är en ny metod för att simulera integritetshot vidanvändning av molntjänster, en metod som kan användas till att identifierade integritetskrav som överensstämmer med gällande dataskyddslagar. Vårmetod används sedan för att föreslå ett ramverk som möter de integritetskravsom ställs för att hantera data inom området “genomik”. Genomik handlari korthet om hälsodata avseende arvsmassan (DNA) hos enskilda individer.Vårt andra större bidrag är ett system för att bevara integriteten vid publice-ring av biologiska provdata. Systemet har fördelen att kunna sammankopplaflera olika uppsättningar med data. Avhandlingen fortsätter med att före-slå och beskriva ett system kallat ScaBIA, ett integritetsbevarande systemför hjärnbildsanalyser processade via molntjänster. Avhandlingens avslutan-de kapitel beskriver ett nytt sätt för kvantifiering och minimering av risk vid“kernel exploitation” (“utnyttjande av kärnan”). Denna nya ansats är ävenett bidrag till utvecklingen av ett nytt system för (Call interposition referencemonitor for Lind - the dual layer sandbox).
QC 20160516
Mathew, George. "A Perturbative Decision Making Framework for Distributed Sensitive Data". Diss., Temple University Libraries, 2014. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/269109.
Texto completoPh.D.
In various business domains, intelligence garnered from data owned by peer institutions can provide useful information. But, due to regulations, privacy concerns and legal ramifications, peer institutions are reluctant to share raw data. For example, in medical domain, HIPAA regulations, Personally Identifiable Information and privacy issues are impediments to data sharing. However, intelligence can be learned from distributed data sets if their key characteristics are shared among desired parties. In scenarios where samples are rare locally, but adequately available collectively from other sites, sharing key statistics about the data may be sufficient to make proper decisions. The objective of this research is to provide a framework in a distributed environment that helps decision-making using statistics of data from participating sites; thereby eliminating the need for raw data to be shared outside the institution. Distributed ID3-based Decision Tree (DIDT) model building is proposed for effectively building a Decision Support System based on labeled data from distributed sites. The framework includes a query mechanism, a global schema generation process brokered by a clearing-house (CH), crosstable matrices generation by participating sites and entropy calculation (for test) using aggregate information from the crosstable matrices by CH. Empirical evaluations were done using synthetic and real data sets. Due to local data policies, participating sites may place restrictions on attribute release. The concept of "constraint graphs" is introduced as an out of band high level filtering for data in transit. Constraint graphs can be used to implement various data transformations including attributes exclusions. Experiments conducted using constraint graphs yielded results consistent with baseline results. In the case of medical data, it was shown that communication costs for DIDT can be contained by auto-reduction of features with predefined thresholds for near constant attributes. In another study, it was shown that hospitals with insufficient data to build local prediction models were able to collaboratively build a common prediction model with better accuracy using DIDT. This prediction model also reduced the number of incorrectly classified patients. A natural follow up question is: Can a hospital with sufficiently large number of instances provide a prediction model to a hospital with insufficient data? This was investigated and the signature of a large hospital dataset that can provide such a model is presented. It is also shown that the error rates of such a model is not statistically significant compared to the collaboratively built model. When rare instances of data occur in local database, it is quite valuable to draw conclusions collectively from such occurrences in other sites. However, in such situations, there will be huge imbalance in classes among the relevant base population. We present a system that can collectively build a distributed classification model without the need for raw data from each site in the case of imbalanced data. The system uses a voting ensemble of experts for the decision model, where each expert is built using DIDT on selective data generated by oversampling of minority class and undersampling of majority class data. The imbalance condition can be detected and the number of experts needed for the ensemble can be determined by the system.
Temple University--Theses
Ansell, Peter. "A context sensitive model for querying linked scientific data". Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/49777/1/Peter_Ansell_Thesis.pdf.
Texto completoLibros sobre el tema "Sensitive date"
Office, General Accounting. Computer security: Identification of sensitive systems operated on behalf of ten agencies : congressional requesters. Washington, D.C: The Office, 1989.
Buscar texto completoChristen, Peter, Thilina Ranbaduge y Rainer Schnell. Linking Sensitive Data. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59706-1.
Texto completoPhotopoulos, Constantine. Managing catastrophic loss of sensitive data. Burlington, MA: Syngress Pub., 2008.
Buscar texto completoClaire, Levallois-Barth, ed. Sensitive data protection in the European Union. Bruxelles: Bruylant, 2007.
Buscar texto completoOffice, General Accounting. Computer security: DEA is not adequately protecting sensitive drug enforcement data : report to the Chairman, Government Information, Justice, and Agriculture Subcommittee, Committee on Government Operations, House of Representatives. Washington, D.C: The Office, 1992.
Buscar texto completoGreat Britain. Parliament. House of Commons. Second Standing Committee on Delegated Legislation. Draft Data Protection (Processing of Sensitive Personal Data) (Elected Representatives) Order 2002. London: Stationery Office, 2002.
Buscar texto completoDaniel, Wayne W. Collecting sensitive data by randomized response: An annotated bibliography. 2a ed. Atlanta, Ga: Georgia State Univ. Business Press, 1993.
Buscar texto completoComputer Security Analysts (Firm : Alexandria, Va.), ed. A Methodology for certifying sensitive computer applications within federal agencies. Alexandria, Va. (PO Box 10255, Alexandria 22310): Computer Security Analysts, 1986.
Buscar texto completoE, Tracy Paul, ed. Randomized response: A method for sensitive surveys. Beverly Hills: Sage Publications, 1986.
Buscar texto completoStaszkiewicz, Maddison. Using Online Surveys to Capture Time-Sensitive Data in a Low-Resource Setting. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529601831.
Texto completoCapítulos de libros sobre el tema "Sensitive date"
Christen, Peter, Thilina Ranbaduge y Rainer Schnell. "Introduction". En Linking Sensitive Data, 3–25. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59706-1_1.
Texto completoChristen, Peter, Thilina Ranbaduge y Rainer Schnell. "Computational Efficiency". En Linking Sensitive Data, 253–85. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59706-1_10.
Texto completoChristen, Peter, Thilina Ranbaduge y Rainer Schnell. "Practical Considerations". En Linking Sensitive Data, 289–321. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59706-1_11.
Texto completoChristen, Peter, Thilina Ranbaduge y Rainer Schnell. "Empirical Evaluation". En Linking Sensitive Data, 323–44. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59706-1_12.
Texto completoChristen, Peter, Thilina Ranbaduge y Rainer Schnell. "Real-world Applications". En Linking Sensitive Data, 345–57. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59706-1_13.
Texto completoChristen, Peter, Thilina Ranbaduge y Rainer Schnell. "Future Research Challenges and Directions". En Linking Sensitive Data, 361–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59706-1_14.
Texto completoChristen, Peter, Thilina Ranbaduge y Rainer Schnell. "Regulatory Frameworks". En Linking Sensitive Data, 27–45. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59706-1_2.
Texto completoChristen, Peter, Thilina Ranbaduge y Rainer Schnell. "Linking Sensitive Data Background". En Linking Sensitive Data, 47–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59706-1_3.
Texto completoChristen, Peter, Thilina Ranbaduge y Rainer Schnell. "Private Information Sharing Protocols". En Linking Sensitive Data, 81–97. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59706-1_4.
Texto completoChristen, Peter, Thilina Ranbaduge y Rainer Schnell. "Assessing Privacy and Risks". En Linking Sensitive Data, 99–122. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59706-1_5.
Texto completoActas de conferencias sobre el tema "Sensitive date"
Zhang, Liwei, Tong Zhang, Wenxue Wu, Xiaoqin Feng, Guoxi Lin y Fengyuan Ren. "Fault- Tolerant Cyclic Queuing and Forwarding in Time-Sensitive Networking". En 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE), 1–6. IEEE, 2024. http://dx.doi.org/10.23919/date58400.2024.10546689.
Texto completoChandran, Sandeep, Smruti R. Sarangi y Preeti Ranjan Panda. "Space Sensitive Cache Dumping for Post-silicon Validation". En Design Automation and Test in Europe. New Jersey: IEEE Conference Publications, 2013. http://dx.doi.org/10.7873/date.2013.113.
Texto completoOttlik, Sebastian, Christoph Gerum, Alexander Viehl, Wolfgang Rosenstiel y Oliver Bringmann. "Context-sensitive timing automata for fast source level simulation". En 2017 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2017. http://dx.doi.org/10.23919/date.2017.7927042.
Texto completoLi, Ang, Peng Li, Tingwen Huang y Edgar Sanchez-Sinencio. "Noise-sensitive feedback loop identification in linear time-varying analog circuits". En 2017 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2017. http://dx.doi.org/10.23919/date.2017.7927190.
Texto completoSchmidt, Tim, Zhongqi Cheng y Rainer Domer. "Port call path sensitive conflict analysis for instance-aware parallel SystemC simulation". En 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2018. http://dx.doi.org/10.23919/date.2018.8342034.
Texto completoGuerra, R. y G. Fohler. "On-line scheduling of target sensitive periodic tasks with the gravitational task model". En 2012 Design, Automation & Test in Europe Conference & Exhibition (DATE 2012). IEEE, 2012. http://dx.doi.org/10.1109/date.2012.6176536.
Texto completoChen, Shu-Yung, Chien-Hao Chen y Ren-Song Tsay. "An activity-sensitive contention delay model for highly efficient deterministic full-system simulations". En Design Automation and Test in Europe. New Jersey: IEEE Conference Publications, 2014. http://dx.doi.org/10.7873/date.2014.226.
Texto completoFlich, José, Giovanni Agosta, Philipp Ampletzerz, David Atienza Alonso, Carlo Brandolese, Alessandro Cilardo, William Fornaciari et al. "Enabling HPC for QoS-sensitive Applications: The MANGO Approach". En Proceedings of the 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE). Singapore: Research Publishing Services, 2016. http://dx.doi.org/10.3850/9783981537079_1019.
Texto completoPeeck, Jonas, Johannes Schlatow y Rolf Ernst. "Online latency monitoring of time-sensitive event chains in safety-critical applications". En 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2021. http://dx.doi.org/10.23919/date51398.2021.9474109.
Texto completoZhang, Xinyan, Kai Shant, Zhipeng Tan y Dan Feng. "CSLE: A Cost-sensitive Learning Engine for Disk Failure Prediction in Large Data Centers". En 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2022. http://dx.doi.org/10.23919/date54114.2022.9774751.
Texto completoInformes sobre el tema "Sensitive date"
Taylor, Christopher Y. y Eric V. Walton. Date Sensitive Computing Problems: Understanding the Threat. Fort Belvoir, VA: Defense Technical Information Center, agosto de 1998. http://dx.doi.org/10.21236/ada375686.
Texto completoTaylor, Christopher Y. y Eric V. Walton. Date Sensitive Computing Problems: Dangers and Opportunities. Fort Belvoir, VA: Defense Technical Information Center, agosto de 1998. http://dx.doi.org/10.21236/ada375687.
Texto completoToomey, Rickard, Vincent Santucci, Justin Tweet, Vincent Santucci, Rickard Toomey y Justin Tweet. Mammoth Cave National Park: Paleontological resource inventory (sensitive version). National Park Service, 2024. http://dx.doi.org/10.36967/2306041.
Texto completoCohen, Yuval, Christopher A. Cullis y Uri Lavi. Molecular Analyses of Soma-clonal Variation in Date Palm and Banana for Early Identification and Control of Off-types Generation. United States Department of Agriculture, octubre de 2010. http://dx.doi.org/10.32747/2010.7592124.bard.
Texto completoAlders, George. L51630A In-Line Detection and Sizing of Stress Corrosion Cracks Using EMAT Ultrasonics - Phase II. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), noviembre de 1991. http://dx.doi.org/10.55274/r0011370.
Texto completoKeller, Dr Jared Robert. How do data institutions facilitate safe access to sensitive data? Open Data Institute, septiembre de 2021. http://dx.doi.org/10.61557/heje5130.
Texto completoMarchant, Christian, Ryan Kirkpatrick y David Ober. Coincidence processing of photon-sensitive mapping lidar data. Engineer Research and Development Center (U.S.), febrero de 2020. http://dx.doi.org/10.21079/11681/35599.
Texto completoFalk, J. y M. Kucherawy, eds. Redaction of Potentially Sensitive Data from Mail Abuse Reports. RFC Editor, abril de 2012. http://dx.doi.org/10.17487/rfc6590.
Texto completoPayton, Jamie, Gruia-Catalin Roman y Christine Julien. Simplifying Context-Aware Agent Coordination Using Context-Sensitive Data Structures. Fort Belvoir, VA: Defense Technical Information Center, enero de 2004. http://dx.doi.org/10.21236/ada484172.
Texto completoBirk, Steffen, Christian Griebler, Johannes C. Haas, Alice Retter, Ainur Kokimova, Constanze Englisch, Santiago Gaviria, Johannes Grath, Heike Brielmann y Christine Stumpp. Impact of extreme hydrological events on the quantity and quality of groundwater in alpine regions – multiple-index application for an integrative hydrogeo-ecological assessment. Verlag der Österreichischen Akademie der Wissenschaften, septiembre de 2023. http://dx.doi.org/10.1553/ess-integrative-groundwater-assessment.
Texto completo