Relevant bibliographies by topics / Bug

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Academic literature on the topic 'Bug'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 January 2023

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Journal articles on the topic "Bug"

Locke, Susannah F. "Bug vs. Bug." Scientific American 300, no. 2 (February 2009): 31. http://dx.doi.org/10.1038/scientificamerican0209-31c.

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Nogrady, Bianca. "Bug for a Bug for a Bug." Scientific American 300, no. 3 (March 2009): 17. http://dx.doi.org/10.1038/scientificamerican0309-17.

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Cherry, Ron. "Interrelationship of Big-Eyed Bugs (Hemiptera: Lygaeidae) and Southern Chinch Bugs (Hemiptera: Lygaeidae) in Florida Lawns2." Journal of Entomological Science 40, no. 4 (October 1, 2005): 385–89. http://dx.doi.org/10.18474/0749-8004-40.4.385.

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The southern chinch bug, Blissus insularis Barber, is the most important insect pest of St. Augustinegrass, Stenotaphrum secundatum (Walt.) Kuntze. Fifteen chinch bug infestations in St. Augustinegrass lawns in Florida were sampled by vacuuming. Additional vacuum samples were taken in 15 randomly selected St. Augustinegrass lawns. The big-eyed bug, Geocoris uliginosus Say, was the most frequent and abundant big-eyed bug found at the infestations. Data showed that big-eyed bugs (Geocoris spp.) were highly aggregated at chinch bug infestations. Furthermore, there was a significant positive correlation between numbers of chinch bugs and big-eyed bugs at chinch bug infestations showing that big-eyed bugs had a numerical predator response to increasing chinch bug populations.

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Morrison, Hope. "Big Bug by Henry Cole." Bulletin of the Center for Children's Books 67, no. 10 (2014): 507. http://dx.doi.org/10.1353/bcc.2014.0477.

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Wells, William. "Sequence of a big bug." Genome Biology 1 (2000): spotlight—20000901–04. http://dx.doi.org/10.1186/gb-spotlight-20000901-04.

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Gibson, Cara M. "A big, bug science party." Science 355, no. 6321 (January 12, 2017): 141.3–141. http://dx.doi.org/10.1126/science.aam6439.

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Ellison, Katherine. "A bug-eat-bug world." Frontiers in Ecology and the Environment 2, no. 2 (March 2004): 112. http://dx.doi.org/10.1890/1540-9295(2004)002[0112:abw]2.0.co;2.

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Ismael, Jenann. "Raid! Dissolving the Big, Bad Bug." Nous 42, no. 2 (June 2008): 292–307. http://dx.doi.org/10.1111/j.1468-0068.2008.00681.x.

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Ostrovskiy, Grigory, and Amos J. Shemesh. "Contents of a Bug-Out Bag." Prehospital and Disaster Medicine 33, no. 6 (November 5, 2018): 647–49. http://dx.doi.org/10.1017/s1049023x18000948.

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AbstractA large number of civilian agencies have published guides and recommendations on how to assemble personal and family emergency kits. However, the kits resulting from following these guidelines are impractical, particularly in the event evacuation becomes necessary. This report describes an alternative approach to assembling an emergency kit.OstrovskiyG, ShemeshAJ. Contents of a bug-out bag. Prehosp Disaster Med. 2018;33(6):647–649.

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Cooper-Jones, Tim. "Hum Bug - there is a millennium bug." Nursing Standard 12, no. 36 (May 27, 1998): 10. http://dx.doi.org/10.7748/ns.12.36.10.s22.

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Dissertations / Theses on the topic "Bug"

Adhikarla, Sridhar. "Automated Bug Classification. : Bug Report Routing." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166224.

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With the growing software technologies companies tend to develop automated solutions to save time and money. Automated solutions have seen tremendous growth in the software industry and have benefited from extensive machine learning research. Although extensive research has been done in the area of automated bug classification, with the new data being collected, more precise methods are yet to be developed. An automated bug classifier will process the content of the bug report and assign it to the person or department that would fix the problem. A bug report typically contains an unstructured text field where the problem is described in detail. A lot of research regarding information extraction from such text fields has been done. This thesis uses a topic modeling technique, Latent Dirichlet Allocation (LDA), and a numerical statistic Term Frequency - Inverse Document Frequency (TF-IDF), to generate two different features from the unstructured text fields of the bug report. A third set of features was created by concatenating the TF-IDF and the LDA features. The class distribution of the data used in this thesis changes over time. To explore if time has an impact on the prediction, the age of the bug report was introduced as a feature. The importance of this feature, when used along with the LDA and TF-IDF features, was also explored in this thesis. These generated feature vectors were used as predictors to train three different classification models; multinomial logistic regression, dense neural networks, and DO-probit. The prediction of the classifiers, for the correct department to handle a bug, was evaluated on the accuracy and the F1-score of the prediction. For comparison, the predictions from a Support Vector Machine (SVM) using a linear kernel was treated as the baseline. The best results for the multinomial logistic regression and the dense neural networks classifiers were obtained when the TF-IDF features of the bug reports were used as predictors. Among the three classifiers trained the dense neural network had the best performance, though the classifier was not able to perform better than the SVM baseline. Using age as a feature did not give a significant improvement in the predictive performance of the classifiers, but was able to identify some interesting patterns in the data. Further research on other ways of using the age of the bug reports could be promising.

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Cordell, Susan, and TP Baxter. "The Conenose Bug (AKA "The Kissing Bug")." College of Agriculture and Life Sciences, University of Arizona (Tucson, AZ), 1999. http://hdl.handle.net/10150/144783.

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One would not suspect that an insect with the congenial nickname of kissing bug could cause life-threatening allergic reactions (anaphylaxis) in sensitive individuals. But anaphylactic shock can be the result of the bite of Triatoma species, also known as the conenose bug, kissing bug, assassin bug, Mexican bedbug, and the Wallapai tiger. This publication discusses the identification, habitat, and the conenose bite of this insect, as well as the controlling method used to reduce theie numbers.

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Connelly, Rita. "Whiteflies-the Tiny Bug with the Big Bite." College of Agriculture, University of Arizona (Tucson, AZ), 1992. http://hdl.handle.net/10150/295733.

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Austli, Viktor, and Elin Hernborg. "Standardization of Bug Validation." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-34801.

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The usage of the Internet is widely implemented all over the world in a number of concepts. This generates a demand of establishing security as to sustain the integrity of data. In this thesis a service will be presented which can be used to identify various web vulnerabilities in order to regulate these and therefore prevent exploitation. As the world is today the increase of technical implementation provides with a growing amount of security flaws, this affect the organizations which may have to increase their resource financing in an effort to counter these. But what if a tremendous amount of work could be automated and avoid organizations having to spend an enormous amount of finances validating security flaws reported to them? What if these flaws could be validated in a more effective manner? With this tool being establish an individual will no longer require advanced technical knowledge in order to identify whether a web vulnerability is present or not but instead have an automated test perform the procedure for them.

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Chen, Jau-Yuan. "Mobile Energy Bug Diagnosis." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366382081.

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Svahn, Caroline. "Automated Bug Report Routing." Thesis, Linköpings universitet, Statistik och maskininlärning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139037.

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As the software industry grows larger by the minute, the need for automated solutions within bug report management is on the rise. Although some research has been conducted in the area of bug handling, new, faster or more precise approaches are yet to be developed. A bug report typically contains a free text observations field where the issue can be described by a human. Research regarding processing of this type of field is extensive, however, bug reports are often accompanied with system log files which have been given less attention so far. In the 4G LTE telecommunications network, the available system log files are many and several are likely to aid the routing of bug reports. In this thesis, one system log file was chosen to be evaluated; the alarm log. The alarm logs are time series count data containing alarms raised by the system. The alarm log data have been pre-processed with data mining techniques. The Apriori algorithm has been used to mine for specific alarms and alarming objects which indicates that the bug report should be solved by a particular developer group. We extend the Apriori algorithm to a temporal setting by using a customised time dependent confidence measure. To further mine for interesting sequences of events in the logs, the sequence mining approach SPADE has been used. The extracted class-associated sequences from both pre-processing approaches are transformed into binary features possible to use as predictors in any prediction model. The results have been evaluated by predicting the correct developer group with two different methods; logistic regression and DO-probit. Logistic regression was regularised with the elastic net penalty to avoid computational issues as well as handling the sparse covariate set. DO-probit was used with a horseshoe prior; it is well suited for the sparse covariate regression problem as it is customised to obtain signals in sparse, noisy data. The results indicate that a data mining approach for processing alarm logs is promising. The results show that the rules obtained with the Apriori mining process are suitable for mining the alarm logs as most binary representations of the rules used as covariates in logistic regression are kept in the equations for the expected classes with strongly positive coefficients. Although, the overall improvement in accuracy from using the alarms logs in addition to the learned topics from free text fields is modest, the alarm logs are concluded to be a good complement to the free text information as some Apriori covariates appears to be better suited to predict some classes than some topics.

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Gouge, Dawn, and Carl Olson. "Choosing a Bug Repellent." College of Agriculture and Life Sciences, University of Arizona (Tucson, AZ), 2011. http://hdl.handle.net/10150/146730.

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"Bugs @ Home series Formerly titled: Insect Repellents; Originally published: 2003"
This publication describes how to use insect repellents safely and effectively. It offers comparisons between repellents available to the public and gives advice on which products are suitable for children.

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Auwal, Bilyaminu Romo. "Improving the quality of bug data in software repositories." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13655.

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Context : Researchers have increasingly recognised the benefit of mining software repositories to extract information. Thus, integrating a version control tool (VC tool) and bug tracking tool (BT tool) in mining software repositories as well as synchronising missing bug tracking data (BT data) and version control log (VC log) becomes of paramount importance, in order to improve the quality of bug data in software repositories. In this way, researchers can do good quality research for software project benefit especially in open source software projects where information is limited in distributed development. Thus, shared data to track the issues of the project are not common. BT data often appears not to be mirrored when considering what developers logged as their actions, resulting in reduced traceability of defects in the development logs (VC logs). VC system (Version control system) data can be enhanced with data from bug tracking system (BT system), because VC logs reports about past software development activities. When these VC logs and BT data are used together, researchers can have a more complete picture of a bug’s life cycle, evolution and maintenance. However, current BT system and VC systems provide insufficient support for cross-analysis of both V Clogs and BT data for researchers in empirical software engineering research: prediction of software faults, software reliability, traceability, software quality, effort and cost estimation, bug prediction, and bug fixing. Aims and objectives: The aim of the thesis is to design and implement a tool chain to support the integration of a VC tool and a BT tool, as well as to synchronise the missing VC logs and BT data of open-source software projects automatically. The syncing process, using Bicho (BT tool) and CVSAnalY (VC tool), will be demonstrated and evaluated on a sample of 344 open source software (OSS) projects. Method: The tool chain was implemented and its performance evaluated semi-automatically. The SZZ algorithm approach was used to detect and trace BT data and VC logs. In its formulation, the algorithm looks for the terms "Bugs," or "Fixed" (case-insensitive) along with the ’#’ sign, that shows the ID of a bug in the VC system and BT system respectively. In i addition, the SZZ algorithm was dissected in its formulation and precision and recall analysed for the use of “fix”, “bug” or “# + digit” (e.g., #1234), was detected was detected when tracking possible bug IDs from the VC logs of the sample OSS projects. Results: The results of this analysis indicate that use of “# + digit” (e.g., #1234) is more precise for bug traceability than the use of the “bug” and “fix” keywords. Such keywords are indeed present in the VC logs, but they are less useful when trying to connect the development actions with the bug traces – that is, their recall is high. Overall, the results indicate that VC log and BT data retrieved and stored by automatic tools can be tracked and recovered with better accuracy using only a part of the SZZ algorithm. In addition, the results indicate 80-95% of all the missing BT data and VC logs for the 344 OSS projects has been synchronised into Bicho and CVSAnalY database respectively. Conclusion: The presented tool chain will eliminate and avoid repetitive activities in traceability tasks, as well as software maintenance and evolution. This thesis provides a solution towards the automation and traceability of BT data of software projects (in particular, OSS projects) using VC logs to complement and track missing bug data. Synchronising involves completing the missing data of bug repositories with the logs de tailing the actions of developers. Synchronising benefit various branches of empirical software engineering research: prediction of software faults, software reliability, traceability, software quality, effort and cost estimation, bug prediction ,and bug fixing.

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Kingdon, Lorraine B. "The Bug of the Year." College of Agriculture, University of Arizona (Tucson, AZ), 1993. http://hdl.handle.net/10150/295734.

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Anvik, John. "Assisting bug report triage through recommendation." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/265.

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A key collaborative hub for many software development projects is the issue tracking system, or bug repository. The use of a bug repository can improve the software development process in a number of ways including allowing developers who are geographically distributed to communicate about project development. However, reports added to the repository need to be triaged by a human, called the triager, to determine if reports are meaningful. If a report is meaningful, the triager decides how to organize the report for integration into the project's development process. We call triager decisions with the goal of determining if a report is meaningful, repository-oriented decisions, and triager decisions that organize reports for the development process, development-oriented decisions. Triagers can become overwhelmed by the number of reports added to the repository. Time spent triaging also typically diverts valuable resources away from the improvement of the product to the managing of the development process. To assist triagers, this dissertation presents a machine learning approach to create recommenders that assist with a variety of development-oriented decisions. In this way, we strive to reduce human involvement in triage by moving the triager's role from having to gather information to make a decision to that of confirming a suggestion. This dissertation introduces a triage-assisting recommender creation process that can create a variety of different development-oriented decision recommenders for a range of projects. The recommenders created with this approach are accurate: recommenders for which developer to assign a report have a precision of 70% to 98% over five open source projects, recommenders for which product component the report is for have a recall of 72% to 92%, and recommenders for who to add to the cc: list of a report that have a recall of 46% to 72%. We have evaluated recommenders created with our triage-assisting recommender creation process using both an analytic evaluation and a field study. In addition, we present in this dissertation an approach to assist project members to specify the project-specific values for the triage-assisting recommender creation process, and show that such recommenders can be created with a subset of the repository data.

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Books on the topic "Bug"

Big bug, little bug. Edina, Minn: ABDO Pub., 2006.

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McGuire, Leslie. Big bug, little bug. New York: Little & Woods Press, 1991.

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Big bug and little bug. Glendale, California: Age of Learning, Inc., 2012.

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ill, Cole Henry, ed. Big bug. New York: Little Simon, 2014.

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Barbara, Taylor. Big bug book. London: Black, 1993.

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Facklam, Margery. Big Bug Book. London: Little, Brown, 1994.

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ill, Facklam Paul, ed. The big bug book. Boston: Little, Brown, 1994.

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Reed, Teresa. The big bug hunt. New York: Aladdin Paperbacks, 1996.

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ill, Barron Rex, ed. The Big Bug Ball. New York: Putnam's Sons, 1999.

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Young, Caroline. The big bug search. London: Usborne Publishing Ltd., 1997.

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Book chapters on the topic "Bug"

Nahler, Gerhard. "bug." In Dictionary of Pharmaceutical Medicine, 19. Vienna: Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_145.

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Weik, Martin H. "bug." In Computer Science and Communications Dictionary, 151. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_1920.

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"Bug against Bug." In Big Fleas Have Little Fleas, 68–82. University of Arizona Press, 2022. http://dx.doi.org/10.2307/j.ctv2p5zn6d.10.

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"bug." In Dictionary Geotechnical Engineering/Wörterbuch GeoTechnik, 171. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41714-6_23568.

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"Praying for the Hell’s Angel." In BUG, 48–49. Gallaudet University Press, 2009. http://dx.doi.org/10.2307/j.ctv2rh288c.21.

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"Engineering Efficient Institutions." In BUG, 154–55. Gallaudet University Press, 2009. http://dx.doi.org/10.2307/j.ctv2rh288c.83.

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"Koko Want." In BUG, 57–58. Gallaudet University Press, 2009. http://dx.doi.org/10.2307/j.ctv2rh288c.27.

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"I Worship Cochlear Implants!" In BUG, 68–69. Gallaudet University Press, 2009. http://dx.doi.org/10.2307/j.ctv2rh288c.32.

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"Another Problem with “Deaf People Can. …”." In BUG, 180–81. Gallaudet University Press, 2009. http://dx.doi.org/10.2307/j.ctv2rh288c.97.

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"Footballs at $1,400 Each." In BUG, 117–18. Gallaudet University Press, 2009. http://dx.doi.org/10.2307/j.ctv2rh288c.58.

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Conference papers on the topic "Bug"

Williams, C. C. "Bug driven bug finders." In "International Workshop on Mining Software Repositories (MSR 2004)" W17S Workshop - 26th International Conference on Software Engineering. IEE, 2004. http://dx.doi.org/10.1049/ic:20040479.

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Chen, Liguo, Xiaobo Wang, and Chao Liu. "Improving Bug Assignment with Bug Tossing Graphs and Bug Similarities." In 2010 International Conference on Biomedical Engineering and Computer Science (ICBECS). IEEE, 2010. http://dx.doi.org/10.1109/icbecs.2010.5462287.

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Park, Jin Wan. "Bug." In ACM SIGGRAPH 98 Electronic art and animation catalog. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/281388.281820.

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Wang, Junjie, Yuchao Huang, Song Wang, and Qing Wang. "Find bugs in static bug finders." In ICPC '22: 30th International Conference on Program Comprehension. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3524610.3527899.

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Wang, Lu, Xiaobing Sun, Jingwei Wang, Yucong Duan, and Bin Li. "Construct Bug Knowledge Graph for Bug Resolution." In 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C). IEEE, 2017. http://dx.doi.org/10.1109/icse-c.2017.102.

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Nichols, Brent D. "Augmented bug localization using past bug information." In the 48th Annual Southeast Regional Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1900008.1900090.

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Jeong, Gaeul, Sunghun Kim, and Thomas Zimmermann. "Improving bug triage with bug tossing graphs." In the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1595696.1595715.

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Taylor, K., and S. M. LaValle. "I-Bug: An intensity-based bug algorithm." In 2009 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2009. http://dx.doi.org/10.1109/robot.2009.5152728.

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Bryce, Renee, Quentin Mayo, Aaron Andrews, Daniel Bokser, Michael Burton, Chelynn Day, Jessica Gonzolez, and Tara Noble. "Bug catcher." In Proceeding of the 44th ACM technical symposium. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2445196.2445348.

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Bodén, Marie, Stephen Viller, and Shelley Dole. "Spelling Bug." In the 22nd Conference of the Computer-Human Interaction Special Interest Group of Australia. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1952222.1952295.

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Reports on the topic "Bug"

Dorman, Seth, and Sally Taylor. Tarnished Plant Bug. Blacksburg, VA: Virginia Cooperative Extension, August 2019. http://dx.doi.org/10.21061/ento-329np.

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Welzl, M. The Quantum Bug. RFC Editor, April 2020. http://dx.doi.org/10.17487/rfc8774.

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Nawrocki, G. The BUG BITBUS Universal Gateway. Office of Scientific and Technical Information (OSTI), February 1996. http://dx.doi.org/10.2172/205214.

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Dixon, J. Isopach map: Bug Creek Group. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1996. http://dx.doi.org/10.4095/207671.

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Fessenden, T. J. Formal solution for the fields within a beam-bug calibrator. Office of Scientific and Technical Information (OSTI), July 1998. http://dx.doi.org/10.2172/8054.

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Thomas C. Chidsey Jr. PRODUCTION ANALYSIS: CHEROKEE AND BUG FIELDS, SAN JUAN COUNTY, UTAH. Office of Scientific and Technical Information (OSTI), December 2003. http://dx.doi.org/10.2172/835836.

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Thomas C. Chidsey Jr and David E. Eby. THIN SECTION DESCRIPTIONS: CHEROKEE AND BUG FIELDS, SAN JUAN COUNTY, UTAH. Office of Scientific and Technical Information (OSTI), December 2003. http://dx.doi.org/10.2172/835954.

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Ploussard, Q., and T. D. Veselka. Financial Analysis of the 2018 Glen Canyon Dam Bug Flow Experiment. Office of Scientific and Technical Information (OSTI), April 2019. http://dx.doi.org/10.2172/1526634.

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Ploussard, Q., and T. Veselka. Financial Analysis of the 2019 Glen Canyon Dam Bug Flow Experiment. Office of Scientific and Technical Information (OSTI), December 2020. http://dx.doi.org/10.2172/1734861.

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Aigner, John D., Katlin Mooneyham, Christopher McCullough, and Thomas Kuhar. Control of Brown Marmorated Stink Bug with Insecticide-Treated Window Screens. Blacksburg, VA: Virginia Cooperative Extension, January 2021. http://dx.doi.org/10.21061/ento-177np_ento-400np.

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