Relevant bibliographies by topics / Data box

Contents

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

Academic literature on the topic 'Data box'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Data box"

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Waldrop, M. Mitchell. "Data Center in a Box." Scientific American 297, no. 2 (August 2007): 90–93. http://dx.doi.org/10.1038/scientificamerican0807-90.

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Chaudhry, Amir, Jon Crowcroft, Heidi Howard, Anil Madhavapeddy, Richard Mortier, Hamed Haddadi, and Derek McAuley. "Personal Data: Thinking Inside the Box." Aarhus Series on Human Centered Computing 1, no. 1 (October 5, 2015): 4. http://dx.doi.org/10.7146/aahcc.v1i1.21312.

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<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>We are in a ‘personal data gold rush’ driven by advertising being the primary revenue source for most online companies. These companies accumulate extensive personal data about individuals with minimal concern for us, the subjects of this process. This can cause many harms: privacy infringement, personal and professional embarrassment, restricted access to labour markets, restricted access to highest value pricing, and many others. There is a critical need to provide technologies that enable alternative practices, so that individuals can par- ticipate in the collection, management and consumption of their personal data. In this paper we discuss the Databox, a personal networked device (and associated services) that col- lates and mediates access to personal data, allowing us to re- cover control of our online lives. We hope the Databox is a first step to re-balancing power between us, the data subjects, and the corporations that collect and use our data. </span></p></div></div></div>
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Bryan, Elizabeth H. "Activities: Exploring Data with Box Plots." Mathematics Teacher 81, no. 8 (November 1988): 658–63. http://dx.doi.org/10.5951/mt.81.8.0658.

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Zhang, Tonglin, and Baijian Yang. "Box–Cox Transformation in Big Data." Technometrics 59, no. 2 (April 3, 2017): 189–201. http://dx.doi.org/10.1080/00401706.2016.1156025.

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Zhang, Tianwen, Xiaoling Zhang, Jianwei Li, Xiaowo Xu, Baoyou Wang, Xu Zhan, Yanqin Xu, et al. "SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis." Remote Sensing 13, no. 18 (September 15, 2021): 3690. http://dx.doi.org/10.3390/rs13183690.

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SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research state-of-the-art technology of ship detection from synthetic aperture radar (SAR) imagery based on deep learning (DL). According to our investigation, up to 46.59% of the total 161 public reports confidently select SSDD to study DL-based SAR ship detection. Undoubtedly, this situation reveals the popularity and great influence of SSDD in the SAR remote sensing community. Nevertheless, the coarse annotations and ambiguous standards of use of its initial version both hinder fair methodological comparisons and effective academic exchanges. Additionally, its single-function horizontal-vertical rectangle bounding box (BBox) labels can no longer satisfy the current research needs of the rotatable bounding box (RBox) task and the pixel-level polygon segmentation task. Therefore, to address the above two dilemmas, in this review, advocated by the publisher of SSDD, we will make an official release of SSDD based on its initial version. SSDD’s official release version will cover three types: (1) a bounding box SSDD (BBox-SSDD), (2) a rotatable bounding box SSDD (RBox-SSDD), and (3) a polygon segmentation SSDD (PSeg-SSDD). We relabel ships in SSDD more carefully and finely, and then explicitly formulate some strict using standards, e.g., (1) the training-test division determination, (2) the inshore-offshore protocol, (3) the ship-size reasonable definition, (4) the determination of the densely distributed small ship samples, and (5) the determination of the densely parallel berthing at ports ship samples. These using standards are all formulated objectively based on the using differences of existing 75 (161 × 46.59%) public reports. They will be beneficial for fair method comparison and effective academic exchanges in the future. Most notably, we conduct a comprehensive data analysis on BBox-SSDD, RBox-SSDD, and PSeg-SSDD. Our analysis results can provide some valuable suggestions for possible future scholars to further elaborately design DL-based SAR ship detectors with higher accuracy and stronger robustness when using SSDD.
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Jere, Stanley, and Edwin Moyo. "Modelling Epidemiological Data Using Box-Jenkins Procedure." Open Journal of Statistics 06, no. 02 (2016): 295–302. http://dx.doi.org/10.4236/ojs.2016.62025.

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Tominaga, Yukio. "Data Structure Comparison Using Box Counting Analysis." Journal of Chemical Information and Computer Sciences 38, no. 5 (September 1998): 867–75. http://dx.doi.org/10.1021/ci9802070.

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Price, W. Nicholson. "Big data and black-box medical algorithms." Science Translational Medicine 10, no. 471 (December 12, 2018): eaao5333. http://dx.doi.org/10.1126/scitranslmed.aao5333.

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Kim, Myung-Geun. "Outliers in Multivariate Box-Cox Transformed Data." Communications for Statistical Applications and Methods 11, no. 1 (April 1, 2004): 189–96. http://dx.doi.org/10.5351/ckss.2004.11.1.189.

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Luo, Tianren, Xueyong Li, Kun Ma, and Xiaoying Luo. "Toward mobile smart data file protection box." International Journal of Autonomic Computing 2, no. 3 (2017): 282. http://dx.doi.org/10.1504/ijac.2017.086992.

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Dissertations / Theses on the topic "Data box"

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Cata, Villa Marcel. "3D Bounding Box Detection from Monocular Images." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261593.

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Object detection is particularly important in robotic applications that require interaction with the environment. Although 2D object detection methods obtain accurate results, these are not enough to provide a complete description of the 3D scenario. Therefore, many models have recently showed promising progress in this challenging field. In this work, the goal is to predict 3D bounding boxes from single images without using temporal data or any explicit depth estimation. We propose an approach for 3D monocular object detection based on Deep3DBox. We replace the geometric constraints taken into account to predict the 3D location of objects by a deep learning module. Moreover, we undertake a study on the different parameters for the modules that are used to predict dimensions and orientation of objects. We conduct experiments in order to search for the best hyperparameters of our model for KITTI cars and we report and compare our results on KITTI and the challenging NuScenes benchmarks for cars and pedestrians with other state of the art methods. Therefore, we conclude that our approach performs on par with similar methods and improves Deep3DBox results.
Objektsdetektion är särskild viktigt i robotikapplikationer som kräver interaktion med miljön. Fastän metoder för objektssdetektion i 2D ger precisa resultat, så krävs det mer för en komplett beskrivning av 3D-scenariot. Därför har flera modeller nyligen visat lovande framsteg i detta utmanande område. I detta arbete är målet att förutsäga inkapslande 3D-lådor från en bild utan användning av temporal data eller explicit beräkning av djup. Tillvägagångssättet vi föreslår för 3D monokulär objektsdetektion baseras på Deep3DBox. Vårt mål är att ersätta de geometriska gränserna som har använts för förutsägning av 3D-position med en djupinlärningsmodul. Dessutom genomför vi en studie på olika modulparametrar som används för förutsägelse av dimension och orientering av objekt. Vi gör experiment för att finna de bästa hyperparametrarna för vår model för KITTI-bilar och vi rapporterar och jämför våra resultat med KITTI och NuScenes utmanande riktmärken för bilar och fotgängare med andra toppresterande metoder. Vi kan därför dra slutsatsen att vårt tillvägagångssätt presterar likvärdigt med liknande metoder och förbättrar Deep3DBox-resultaten.
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Bäck, Marcus. "Grey-Box Modelling of a Quadrotor Using Closed-Loop Data." Thesis, Linköpings universitet, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-123488.

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In this thesis a quadrotor is studied and a linear model is derived using grey-box estimation, a discipline in system identification where a model structure based on physical relations is used and the parameters are estimated using input-output measurements. From IMU measurements and measured PWM signals to the four motors, a direct approach using the prediction-error method is applied. To investigate the impact of the unknown controller the two-stage method, a closed-loop approach in system identification,  is applied as well. The direct approach was enough for estimating the model parameters. The resulting model manages to simulate the major dynamics for the vertical acceleration and the angular rates well enough  for future control design.
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Lima, Lucas Albertins de. "Test case prioritization based on data reuse for Black-box environments." Universidade Federal de Pernambuco, 2009. https://repositorio.ufpe.br/handle/123456789/1922.

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Made available in DSpace on 2014-06-12T15:53:11Z (GMT). No. of bitstreams: 2 arquivo1906_1.pdf: 1728491 bytes, checksum: 711dbaf0713ac324ffe904a6dace38d7 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Albertins de Lima, Lucas; Cezar Alves Sampaio, Augusto. Test case prioritization based on data reuse for Black-box environments. 2009. Dissertação (Mestrado). Programa de Pós-Graduação em Ciência da Computação, Universidade Federal de Pernambuco, Recife, 2009.
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Khandelwal, Aashish S. "Evaluating Grey-box Models in Highly and Slightly Correlated Imbalanced Data Sets." Ohio University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1290094312.

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Schoberg, Paul R. "Secure ground-based remote recording and archiving of aircraft "Black Box" data." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03sep%5FSchoberg.pdf.

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Thesis (M.S. in Computer Science)--Naval Postgraduate School, September 2003.
Thesis advisor(s): Cynthia E. Irvine, Scott Cote. Includes bibliographical references (p. 169-171). Also available online.
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Sherrell, S. J. "The validation of two box models predicting dense gas dispersion with experimental data." Thesis, University of Liverpool, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.233823.

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Savage, Nicholas Henry. "Studies of the chemical transformation of atmospheric nitrogen and sulphur compounds by comparing data with models." Thesis, University of East Anglia, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302208.

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Zimic, Sheila. "OPENING THE BOX : Exploring the presumptions about the 'Net Generation'." Licentiate thesis, Mittuniversitetet, Institutionen för informationsteknologi och medier, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-12189.

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There are many names or labels which refer to the generation growing up with digital media and these include labels such as ‘Net Generation’ (Tapscott, 1998), ‘digital natives’ (Prensky, 2001), ‘cyberkids’ (Holloway, 2003) and ‘MySpacegeneration’ (Rosen, 2008). The core idea behind these labels is that young people who have grown up surrounded by digital technology are very different to previous generations in their way of using and even thinking about the new digital technology. This appears to be reinforcing a generational divide and makes the assumption that young people can be categorized into one group in relation to their use of ICTs. The approach in this thesis is to empirically explore, in order to nuance, some of these presumptions about the ‘Net Generation’ (defined according to Tapscott). Thus, the research question is: How can the presumptions about the ‘Net Generation’ be nuanced? The following three presumptions have been explored within the three papers included in the thesis: i) The ‘Net Generation’ diverges from previous generations in relation to the use of internet; ii) The ‘Net Generation’ is techno-savvy or digitally competent; iii) The digitally competent ‘Net Geners’ are also digital participants since there is a causal relationship between digital competence and digital participation. The explorations are conducted by using the theoretical concepts ‘digital skills’, ‘self-efficacy’ and ‘participatory culture’. Several hypotheses,deduced from previous research, have been tested on a national representative sample of people born between the years 1978 and 1997 (categorised as the ‘Net Generation’). The results show that ‘Net Geners’ internet usage is diversified;hence, it is simplified to talk about them as a homogeneous group. Those included in the categorisation have different opportunities to participate in the digital society. Their internet usage differs both in terms of how much time they spend and what they do online. Their digital skills and self-efficacy in the use of computers are also different and so is the perceived feeling of participation in the information society. This implies that the ‘Net Geners’ do not have equal conditions in relation to participation in the digital society. However, what is meant by participation is still an unresolved question which requires further exploration.
SAMMANDRAG Idag beskrivs ofta generationen som vuxit upp med internet och digitala medier i termer av att vara en ”internetgeneration” (Tapscott, 1998), "digitala infödingar" (Prensky, 2001), "cyberkids" (Holloway, 2003) eller "MySpace-generation" (Rosen, 2008). Dessa antaganden bygger på att det finns en generationsskillnad i förhållningssätt till internet, d.v.s. att ”internetgenerationen” är de som är kunniga och vana internetanvändare till skillnad från äldre generationer som inte helt naturligt kan ta till sig den nya digitala tekniken. Avhandlingens syfte är att empiriskt undersöka ett antal antaganden om ”internetgenerationen” för att ta reda hur dessa kan nyanseras. Således är frågeställningen: Hur kan antaganden om "internetgenerationen" nyanseras?   Följande tre antaganden har utforskats inom de tre artiklarna som ingår i avhandlingen: i) "internetgenerationen" skiljer sig från tidigare generationer när det gäller användningen av internet, ii) "internetgenerationen" är tekniskt kunniga eller digitalt kompetenta; iii) den digitalt kompetenta "internetgenerationen" är också digitalt delaktig eftersom det finns ett orsakssamband mellan digital kompetens och digital delaktighet. För att empiriskt undersöka antagandena om internetgenerationen har teoretiska koncept så som ”digital skills”, ”self-efficacy” samt ”participatory culture” använts. Ett antal hypoteser som deducerats utifrån tidigare forskning har testats på ett riksrepresentativt urval av personer födda mellan 1978 och 1997 (de som tillhör internetgenerationen). Resultaten visar att det är en förenklad bild att prata om en internetgeneration. De som ingår i den kategoriseringen har olika förutsättningar att delta i det digitala samhället. Deras internetanvändning skiljer sig åt både när det gäller hur mycket tid de spenderar samt vad de gör online. Deras digitala kompetens och självförtroende när det gäller användning av datorer skiljer sig också åt och det gäller även den upplevda känslan av delaktighet i informationssamhället. Detta indikerar att inte alla har samma förutsättningar att vara delaktiga i det digitala samhället. Vad som däremot innebär att vara delaktig är fortfarande en obesvarad fråga som behöver ägnas mer uppmärksamhet i framtida studier.
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Olmo, Segovia Arantxa. "FEM simulation of a steel box culvert test : Comparisons of numerical results with field data." Thesis, KTH, Bro- och stålbyggnad, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-36950.

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Irestål, Erik. "Break out Box for Transmission of Synchronous Video and CAN Data Streams over Gigabit Ethernet." Thesis, Linköping University, Department of Electrical Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-18377.

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Active safety systems for automobiles in the form of camera systems have evolved rapidly the last ten years, Autoliv Electronics in Linköping develops multiple such systems. In their development process there is a need for a Break out Box (BoB) to record and playback video and CAN data as if the camera system was used in an actual automobile. The aim of this thesis has been to develop a BoB for these camera systems. The work has been divided into three phases; identification of requirements, design of the BoB and implementation of a prototype. The project has addressed four known issues with the currently used BoB; bandwidth, modularity, synchronization and usability. The result is a new BoB which is based on an FPGA connecting to a PC over Gigabit Ethernet. The design is an extendible platform for multiple channels of video, CAN data, other serial data and future extensions. A prototype proves the design concept by successfully recording video for the Autoliv NightVision system onto a PC.

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Books on the topic "Data box"

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Gardiner, Caroline. Box office data report 1997. London: Society of London Theatre, 1998.

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Lidstone, Gerald. Box office data report for 2000. London: Society of London Theatre, 2001.

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Lidstone, Gerald. Box office data report for 2001. London: Society of London Theatre, 2002.

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Gardiner, Caroline. Box office data report 1993: Full report. [London]: [Society of London Theatre?], 1994.

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Gardiner, Caroline. Box office data report, 1993: Full report. London: Society ofWest End Theatre, 1994.

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Gardiner, Caroline. Box office data report, 1991: Full report. London: Society ofWest End Theatre, 1991.

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Dickety, Julia. Box office data report 1994: Full report. [London]: [Society of London Theatre], 1995.

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Gardiner, Caroline. Box office data report 1992: Full report. [London]: [Society of London Theatre?], 1993.

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Gardiner, Caroline. Box office data report 1995: Full report. London: Society of London Theatre, 1995.

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Rosenbluth, William. Application and interpretation of black box data and crash dynamics. West Conshohocken, PA: ASTM International, 2009.

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Book chapters on the topic "Data box"

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Cattell, Raymond B. "The Data Box." In Handbook of Multivariate Experimental Psychology, 69–130. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-0893-5_3.

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Boire, Richard. "Black Box Analytics." In Data Mining for Managers, 151–54. New York: Palgrave Macmillan US, 2014. http://dx.doi.org/10.1057/9781137406194_19.

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Cai, Jinghui, Boyang Wang, Xiangfeng Wang, and Bo Jin. "Accelerate Black-Box Attack with White-Box Prior Knowledge." In Intelligence Science and Big Data Engineering. Big Data and Machine Learning, 394–405. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36204-1_33.

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Boyle, Conall. "Display the Data: Box-Plots." In Mastering Statistics with your Microcomputer, 44–51. London: Macmillan Education UK, 1986. http://dx.doi.org/10.1007/978-1-349-18211-4_7.

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Garcés, Kelly, Rubby Casallas, Camilo Álvarez, Edgar Sandoval, Alejandro Salamanca, Fabián Melo, and Juan Manuel Soto. "White-Box Modernization of Legacy Applications." In Model and Data Engineering, 274–87. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45547-1_22.

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Zhang, Hao, Shinji Nakadai, and Kenji Fukumizu. "From Black-Box to White-Box: Interpretable Learning with Kernel Machines." In Machine Learning and Data Mining in Pattern Recognition, 213–27. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96136-1_18.

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Hammer, Barbara, Bassam Mokbel, Frank-Michael Schleif, and Xibin Zhu. "White Box Classification of Dissimilarity Data." In Lecture Notes in Computer Science, 309–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28942-2_28.

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Jin, Rong, Weili Wu, My T. Thai, and Ding-Zhu Du. "Black-Box and Data-Driven Computation." In Black Box Optimization, Machine Learning, and No-Free Lunch Theorems, 163–68. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66515-9_6.

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Deng, Kai, Jiajin Huang, and Jin Qin. "Box4Rec: Box Embedding for Sequential Recommendation." In Advances in Knowledge Discovery and Data Mining, 537–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75765-6_43.

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Gupta, A. K., and J. Tang. "Distribution of Likelihood Criteria and Box Approximation." In Multivariate Statistical Modeling and Data Analysis, 139–45. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3977-6_8.

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Conference papers on the topic "Data box"

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"NETWORK IN A BOX." In International Conference on Data Communication Networking. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002891600550060.

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Henin, Clement, and Daniel Le Metayer. "A Generic Framework for Black-box Explanations." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377863.

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Banks, David, Anthony Wiley, Nicolas Catania, Alastair N. Coles, Duncan Smith, Simon Baynham, Eric Deliot, and Rod Chidzey. "Breaking open the set top box." In Voice, Video, and Data Communications, edited by V. Michael Bove, Jr., Barbara Derryberry, Clifford R. Holliday, Louis S. Lome, Vishal Markandey, Andrew G. Tescher, and Bhaskaran Vasudev. SPIE, 1998. http://dx.doi.org/10.1117/12.300882.

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Wojnowicz, Michael Thomas, and Xuan Zhao. "PROPS: Probabilistic personalization of black-box sequence models." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622574.

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Wei, Wutao, Le Zhang, Qi Ding, and Bingrou Zhou. "Dynamic Bayesian predictive model for box office forecasting." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258405.

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Schikora, M., A. Gning, L. Mihaylova, D. Cremers, W. Koch, and R. Streit. "Box-particle intensity filter." In 9th IET Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications. IET, 2012. http://dx.doi.org/10.1049/cp.2012.0405.

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Ren, Qingjuan, Yilan Zhang, Shuai Huang, Jiancai Meng, and Xueyi Mo. "Data Analysis of Shared Express Box." In International Conference on Education, Economics and Information Management (ICEEIM 2019). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.200401.053.

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Sun, Xiangyu, Jack Davis, Oliver Schulte, and Guiliang Liu. "Cracking the Black Box." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3403367.

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Fan, Lixin, Chang Liu, Yuhao Zhou, Tianyu Zhang, and Qiang Yang. "Interpreting and Evaluating Black Box Models in a Customizable Way." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378081.

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Chow, Ka-Ho, Wenqi Wei, Yanzhao Wu, and Ling Liu. "Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006090.

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Reports on the topic "Data box"

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Hersey, Anne. ChEMBL Deposited Data Set - Malaria Box. EMBL-EBI, January 2013. http://dx.doi.org/10.6019/chembl2028040.

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Hersey, Anne. ChEMBL Deposited Data Set - Malaria Box Vermont. EMBL-EBI, July 2014. http://dx.doi.org/10.6019/chembl3137356.

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Hersey, Anne. ChEMBL Deposited Data Set - Malaria Box Eisai. EMBL-EBI, July 2014. http://dx.doi.org/10.6019/chembl3137361.

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Hersey, Anne. ChEMBL Deposited Data Set - Malaria Box ourexperiment. EMBL-EBI, July 2014. http://dx.doi.org/10.6019/chembl3137547.

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Hersey, Anne. ChEMBL Deposited Data Set - Malaria Box TB screen. EMBL-EBI, January 2013. http://dx.doi.org/10.6019/chembl2094260.

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Hersey, Anne. ChEMBL Deposited Data Set - Malaria Box Schistomiasis screen. EMBL-EBI, September 2013. http://dx.doi.org/10.6019/chembl2363022.

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Hersey, Anne. ChEMBL Deposited Data Set - Malaria Box Northwick Park. EMBL-EBI, July 2014. http://dx.doi.org/10.6019/chembl3137381.

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Hersey, Anne. ChEMBL Deposited Data Set - Malaria Box UCSF DeRisi Lab. EMBL-EBI, April 2014. http://dx.doi.org/10.6019/chembl2448809.

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Loehle, C. Rescaled box counting for the topological analysis of spatial data. Office of Scientific and Technical Information (OSTI), April 1994. http://dx.doi.org/10.2172/10142283.

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Walker, J. Operations manual for reading VAK-3 ultrasonic seals using a data acquisition box. Office of Scientific and Technical Information (OSTI), May 1990. http://dx.doi.org/10.2172/6855647.

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