Relevant bibliographies by topics / Machine learning for information improvement

Contents

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

Academic literature on the topic 'Machine learning for information improvement'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Machine learning for information improvement"

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Zhang, Zhi Feng, Cheng Gan, Xiao Jian Ding, and Zeng Yu Cai. "Research on Optimization Method of Extreme Learning Machine with Application of Information Technology." Advanced Materials Research 859 (December 2013): 23–27. http://dx.doi.org/10.4028/www.scientific.net/amr.859.23.

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Based on the research of extreme learning machine and support vector machine, this paper does the research on the optimization method on extreme learning machine. This paper suggests an optimization model of extreme learning machine based on the improvement of the old model, and this model has obvious improvement on generalization ability and learning parameter ability. This approach can improve the development efficiency in the information technology, the experiment indicate this approach is efficient.
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Shoureshi, R., D. Swedes, and R. Evans. "Learning Control for Autonomous Machines." Robotica 9, no. 2 (1991): 165–70. http://dx.doi.org/10.1017/s0263574700010201.

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SUMMARYToday's industrial machines and manipulators have no capability to learn by experience. Performance and productivity could be greatly enhanced if a machine could modify its operation based on previous actions. This paper presents a learning control scheme that provides the ability for machines to utilize their past experiences. The objective is to have machines mimic the human learning process as closely as possible. A data base is formulated to provide the machine with experience. An optical infrared distance sensor is developed to inform the machine about objects in its working space.
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Sumner, Joel, and Adel Alaeddini. "Analysis of Feature Extraction Methods for Prediction of 30-Day Hospital Readmissions." Methods of Information in Medicine 58, no. 06 (2019): 213–21. http://dx.doi.org/10.1055/s-0040-1702159.

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Abstract Objectives This article aims to determine possible improvements made by feature extraction methods to the machine learning prediction methods for predicting 30-day hospital readmissions. Methods The study evaluates five feature extraction methods including principal component analysis (PCA), kernel principal component analysis (KPCA), isomap, Laplacian eigenmaps, and locality preserving projections (LPPs) for improving the accuracy of nine machine learning prediction methods in predicting 30-day hospital readmissions. The specific prediction methods considered include logistic regress
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MAHAJAN, SHWETA. "News Classification Using Machine Learning." International Journal on Recent and Innovation Trends in Computing and Communication 9, no. 5 (2021): 23–27. http://dx.doi.org/10.17762/ijritcc.v9i5.5464.

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There are plenty of social media webpages and platforms producing the textual data. These different kind of a data needs to be analysed and processed to extract meaningful information from raw data. Classification of text plays a vital role in extraction of useful information along with summarization, text retrieval. In our work we have considered the problem of news classification using machine learning approach. Currently we have a news related dataset which having various types of data like entertainment, education, sports, politics, etc. On this data we have applying classification algorit
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Rahmati, Marzie, and Mohammad Ali Zare Chahooki. "Improvement in bug localization based on kernel extreme learning machine." Journal of Communications Technology, Electronics and Computer Science 5 (April 30, 2016): 1. http://dx.doi.org/10.22385/jctecs.v5i0.77.

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Bug localization uses bug reports received from users, developers and testers to locate buggy files. Since finding a buggy file among thousands of files is time consuming and tedious for developers, various methods based on information retrieval is suggested to automate this process. In addition to information retrieval methods for error localization, machine learning methods are used too. Machine learning-based approach, improves methods of describing bug report and program code by representing them in feature vectors. Learning hypothesis on Extreme Learning Machine (ELM) has been recently ef
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Padovani de Souza, Kleber, João Carlos Setubal, André Carlos Ponce de Leon F. de Carvalho, Guilherme Oliveira, Annie Chateau, and Ronnie Alves. "Machine learning meets genome assembly." Briefings in Bioinformatics 20, no. 6 (2018): 2116–29. http://dx.doi.org/10.1093/bib/bby072.

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Abstract Motivation: With the recent advances in DNA sequencing technologies, the study of the genetic composition of living organisms has become more accessible for researchers. Several advances have been achieved because of it, especially in the health sciences. However, many challenges which emerge from the complexity of sequencing projects remain unsolved. Among them is the task of assembling DNA fragments from previously unsequenced organisms, which is classified as an NP-hard (nondeterministic polynomial time hard) problem, for which no efficient computational solution with reasonable ex
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Manu, Y. M., and G. K. Ravikumar. "Survey on Machine Learning Based Video Analytics Techniques." Journal of Computational and Theoretical Nanoscience 17, no. 11 (2020): 4989–95. http://dx.doi.org/10.1166/jctn.2020.9000.

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Video information has turned into the biggest wellspring of information expended all inclusive. Because of the fast development of applications which are related to video applications and requests of boosting for greater surpassing video administrations, video information volume has expanding violently around the world, which is the serious challenge for media processing, capacity and transmission. Video coding by packing recordings into a lot littler size is also key arrangements; in any case, its advancement has turned out to be soaked somewhat while the pressure proportion consistently deve
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Ji, Meng, Yanmeng Liu, and Tianyong Hao. "Predicting Health Material Accessibility: Development of Machine Learning Algorithms." JMIR Medical Informatics 9, no. 9 (2021): e29175. http://dx.doi.org/10.2196/29175.

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Background Current health information understandability research uses medical readability formulas to assess the cognitive difficulty of health education resources. This is based on an implicit assumption that medical domain knowledge represented by uncommon words or jargon form the sole barriers to health information access among the public. Our study challenged this by showing that, for readers from non-English speaking backgrounds with higher education attainment, semantic features of English health texts that underpin the knowledge structure of English health texts, rather than medical jar
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Karamitsos, Ioannis, Saeed Albarhami, and Charalampos Apostolopoulos. "Applying DevOps Practices of Continuous Automation for Machine Learning." Information 11, no. 7 (2020): 363. http://dx.doi.org/10.3390/info11070363.

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This paper proposes DevOps practices for machine learning application, integrating both the development and operation environment seamlessly. The machine learning processes of development and deployment during the experimentation phase may seem easy. However, if not carefully designed, deploying and using such models may lead to a complex, time-consuming approaches which may require significant and costly efforts for maintenance, improvement, and monitoring. This paper presents how to apply continuous integration (CI) and continuous delivery (CD) principles, practices, and tools so as to minim
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Bagui, Sikha, and Daniel Benson. "Android Adware Detection Using Machine Learning." International Journal of Cyber Research and Education 3, no. 2 (2021): 1–19. http://dx.doi.org/10.4018/ijcre.2021070101.

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Adware, an advertising-supported software, becomes a type of malware when it automatically delivers unwanted advertisements to an infected device, steals user information, and opens other vulnerabilities that allow other malware and adware to be installed. With the rise of more and complex evasive malware, specifically adware, better methods of detecting adware are required. Though a lot of work has been done on malware detection in general, very little focus has been put on the adware family. The novelty of this paper lies in analyzing the individual adware families. To date, no work has been
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Dissertations / Theses on the topic "Machine learning for information improvement"

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Zhang, Ganqin. "Bipartite RankBoost+: An Improvement to Bipartite RankBoost." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case160767885657324.

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Grönberg, David, and Otto Denesfay. "Comparison and improvement of time aware collaborative filtering techniques : Recommender systems." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160360.

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Recommender systems emerged in the mid '90s with the objective of helping users select items or products most suited for them. Whether it is Facebook recommending people you might know, Spotify recommending songs you might like or Youtube recommending videos you might want to watch, recommender systems can now be found in every corner of the internet. In order to handle the immense increase of data online, the development of sophisticated recommender systems is crucial for filtering out information, enhancing web services by tailoring them according to the preferences of the user. This thesis
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Grangier, David. "Machine learning for information retrieval." Lausanne : École polytechnique fédérale de Lausanne, 2008. http://aleph.unisg.ch/volltext/464553_Grangier_Machine_learning_for_information_retrieval.pdf.

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Zheng, Yu. "Machine learning and option implied information." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/57953.

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The thesis consists of three chapters which focus on two broad topics, applying machine learning in finance (Chapters 1 and 2) and extracting implied information from options (Chapter 3). In Chapter 1, I combine the data-driven approach from the machine learning community and economic theory from the finance community to design a deep neural network to estimate the implied volatility surface. Chapter 2 is a second example of applying machine learning in finance. Yang et al. [2017] proposes a gated neural network for pricing European call options. Yang et al. [2017] is rewritten in this chapter us
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Cleland, Andrew Lewis. "Bounding Box Improvement with Reinforcement Learning." PDXScholar, 2018. https://pdxscholar.library.pdx.edu/open_access_etds/4438.

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In this thesis, I explore a reinforcement learning technique for improving bounding box localizations of objects in images. The model takes as input a bounding box already known to overlap an object and aims to improve the fit of the box through a series of transformations that shift the location of the box by translation, or change its size or aspect ratio. Over the course of these actions, the model adapts to new information extracted from the image. This active localization approach contrasts with existing bounding-box regression methods, which extract information from the image only once.
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Johansson, Richard. "Machine learning på tidsseriedataset : En utvärdering av modeller i Azure Machine Learning Studio." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71223.

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In line with technology advancements in processing power and storing capabilities through cloud services, higher demands are set on companies’ data sets. Business executives are now expecting analyses of real time data or massive data sets, where traditional Business Intelligence struggle to deliver. The interest of using machine learning to predict trends and patterns which the human eye can’t see is thus higher than ever. Time series data sets are data sets characterised by a time stamp and a value; for example, a sensor data set. The company with which I’ve been in touch collects data from
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Ayan, Necip Fazil. "Combining linguistic and machine learning techniques for word alignment improvement." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/3126.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.<br>Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Wang, Tianze. "Machine Learning for Constraint Programming." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254660.

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It is well established that designing good heuristics for solving Constraint Programming models requires years of domain experience and a huge amount of trial and error. In this thesis project, we conduct an empirical study of whether Machine Learning and Deep Learning techniques have the potential to help the design of constraint solving heuristics.Specifically, this thesis project examines the potential of Machine Learning and Deep Learning models for the regression task of predicting the makespan and solving time of a Job-Shop Scheduling Problem without actually solving the given Job-Shop S
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Carle, Victor. "Web Scraping using Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281344.

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This thesis explores the possibilities of creating a robust Web Scraping algorithm, designed to continously scrape a specific website even though the HTML code is altered. The algorithm is intended to be used on websites that have a repetitive HTML structure containing data that can be scraped. A repetitive HTML structure often displays; news articles, videos, books, etc. This creates code in the HTML which is repeated many times, as the only thing different between the things displayed are for example titles. A good examplewould be Youtube. The scraper works through using text classification
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Chafik, Sanaa. "Machine learning techniques for content-based information retrieval." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLL008/document.

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Avec l’évolution des technologies numériques et la prolifération d'internet, la quantité d’information numérique a considérablement évolué. La recherche par similarité (ou recherche des plus proches voisins) est une problématique que plusieurs communautés de recherche ont tenté de résoudre. Les systèmes de recherche par le contenu de l’information constituent l’une des solutions prometteuses à ce problème. Ces systèmes sont composés essentiellement de trois unités fondamentales, une unité de représentation des données pour l’extraction des primitives, une unité d’indexation multidimensionnelle
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Books on the topic "Machine learning for information improvement"

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Swain, Debabala, Prasant Kumar Pattnaik, and Pradeep K. Gupta, eds. Machine Learning and Information Processing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1884-3.

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Swain, Debabala, Prasant Kumar Pattnaik, and Tushar Athawale, eds. Machine Learning and Information Processing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4859-2.

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Houser, David Allan. Machine learning as a quality improvement tool. National Library of Canada, 1996.

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MACKAY, DAVID J. C. Information Theory, Inference & Learning Algorithms. Cambridge University Press, 2003.

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Terry, Caelli, ed. A compendium of machine learning. Ablex Pub. Corp., 1996.

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Jebara, Tony. Machine Learning: Discriminative and Generative. Springer US, 2004.

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Machine learning of robot assembly plans. Kluwer Academic Publishers, 1988.

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1951-, Rada R., ed. Machine learning: Applications in expert systems and information retrieval. E. Horwood, 1986.

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Príncipe, J. C. Information theoretic learning: Renyi's entropy and kernel perspectives. Springer, 2010.

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Pathak, Manas A. Privacy-Preserving Machine Learning for Speech Processing. Springer New York, 2013.

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Book chapters on the topic "Machine learning for information improvement"

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Uszkoreit, Hans, Feiyu Xu, and Hong Li. "Analysis and Improvement of Minimally Supervised Machine Learning for Relation Extraction." In Natural Language Processing and Information Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12550-8_2.

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Teja, S., B. Chandrashekhar, Eswar Reddy, et al. "Intuitive Feature Engineering and Machine Learning Performance Improvement in the Banking Domain." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0401-0_28.

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Consoli, Sergio, Luca Tiozzo Pezzoli, and Elisa Tosetti. "Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64583-0_18.

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AbstractThe Global Data on Events, Location, and Tone (GDELT) is a real time large scale database of global human society for open research which monitors worlds broadcast, print, and web news, creating a free open platform for computing on the entire world’s media. In this work, we first describe a data crawler, which collects metadata of the GDELT database in real-time and stores them in a big data management system based on Elasticsearch, a popular and efficient search engine relying on the Lucene library. Then, by exploiting and engineering the detailed information of each news encoded in GDELT, we build indicators capturing investor’s emotions which are useful to analyse the sovereign bond market in Italy. By using regression analysis and by exploiting the power of Gradient Boosting models from machine learning, we find that the features extracted from GDELT improve the forecast of country government yield spread, relative that of a baseline regression where only conventional regressors are included. The improvement in the fitting is particularly relevant during the period government crisis in May-December 2018.
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Le, Van-Minh, and Thi Thanh Ha Hoang. "Improvement of Machine Learning Method by Combining Flow Text and Layout Text in Extracting Information from Scanned Healthcare Documents." In Frontiers in Intelligent Computing: Theory and Applications. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9186-7_28.

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Utgoff, Paul E., James Cussens, Stefan Kramer, et al. "Improvement Curve." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_385.

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Ramasubramanian, Karthik, and Abhishek Singh. "Model Performance Improvement." In Machine Learning Using R. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2334-5_8.

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Ramasubramanian, Karthik, and Abhishek Singh. "Model Performance Improvement." In Machine Learning Using R. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4215-5_8.

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Camastra, Francesco, and Alessandro Vinciarelli. "Machine Learning." In Advanced Information and Knowledge Processing. Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6735-8_4.

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Amari, Shun-ichi. "Machine Learning." In Information Geometry and Its Applications. Springer Japan, 2016. http://dx.doi.org/10.1007/978-4-431-55978-8_11.

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Liu, Tie-Yan. "Machine Learning." In Learning to Rank for Information Retrieval. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-14267-3_22.

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Conference papers on the topic "Machine learning for information improvement"

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Kim, Geon-Hwan, Yeong-Jun Song, and You-Ze Cho. "Improvement of inter-protocol fairness for BBR congestion control using machine learning." In 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2020. http://dx.doi.org/10.1109/icaiic48513.2020.9065259.

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Sumanas, Marius, Algirdas Petronis, Vytautas Bucinskas, et al. "Implementation of Machine Learning Method for Positioning Accuracy Improvement in Industrial Robot." In 2020 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream). IEEE, 2020. http://dx.doi.org/10.1109/estream50540.2020.9108858.

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Azimi, Shelernaz, and Claus Pahl. "Root Cause Analysis and Remediation for Quality and Value Improvement in Machine Learning Driven Information Models." In 22nd International Conference on Enterprise Information Systems. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009783106560665.

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Bittencourt, Jose Luiz, Ralf Bonefeld, Sebastian Scholze, Dragan Stokic, M. Kamal Uddin, and J. L. Martinez Lastra. "Energy efficiency improvement through context sensitive self-learning of machine availability." In 2011 9th IEEE International Conference on Industrial Informatics (INDIN). IEEE, 2011. http://dx.doi.org/10.1109/indin.2011.6034843.

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Hamayel, Mohammad J., Mobarak A. Abu Mohsen, and Mohammed Moreb. "Improvement of personal loans granting methods in banks using machine learning methods and approaches in Palestine." In 2021 International Conference on Information Technology (ICIT). IEEE, 2021. http://dx.doi.org/10.1109/icit52682.2021.9491636.

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Xi, Xia, Zhao Wei, Rui Xiaoguang, et al. "A comprehensive evaluation of air pollution prediction improvement by a machine learning method." In 2015 IEEE International Conference on Service Operations And Logistics, And Informatics (SOLI). IEEE, 2015. http://dx.doi.org/10.1109/soli.2015.7367615.

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Burnap, Alex, Yi Ren, Honglak Lee, Richard Gonzalez, and Panos Y. Papalambros. "Improving Preference Prediction Accuracy With Feature Learning." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35440.

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Motivated by continued interest within the design community to model design preferences, this paper investigates the question of predicting preferences with particular application to consumer purchase behavior: How can we obtain high prediction accuracy in a consumer preference model using market purchase data? To this end, we employ sparse coding and sparse restricted Boltzmann machines, recent methods from machine learning, to transform the original market data into a sparse and high-dimensional representation. We show that these ‘feature learning’ techniques, which are independent from the
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Lu, Jie-Shiou, Hung-Ruey Chen, Ming-Yang Cheng, Ke-Han Su, Li-Wei Cheng, and Mi-Ching Tsai. "Tension control improvement in automatic stator in-slot winding machines using iterative learning control." In 2014 International Conference on Information Science, Electronics and Electrical Engineering (ISEEE). IEEE, 2014. http://dx.doi.org/10.1109/infoseee.2014.6946200.

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Merrill, Nicholas, and Azim Eskandarian. "End-to-End Multi-Task Machine Learning of Vehicle Dynamics for Steering Angle Prediction for Autonomous Driving." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97850.

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Abstract The traditional approaches to autonomous, vision-based vehicle systems are limited by their dependency on robust algorithms, sensor fusion, detailed scene construction, and high-quality maps. End-to-end models offer a means of circumventing these limitations by directly mapping an image input to a steering angle output for lateral control. Existing end-to-end models, however, either fail to capture temporally dynamic information or rely on computationally expensive Recurrent Neural Networks (RNN), which are prone to error accumulation via feedback. This paper proposes a Multi-Task Lea
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Carr, Steven, Nils Jansen, Ralf Wimmer, Alexandru Serban, Bernd Becker, and Ufuk Topcu. "Counterexample-Guided Strategy Improvement for POMDPs Using Recurrent Neural Networks." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/768.

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We study strategy synthesis for partially observable Markov decision processes (POMDPs). The particular problem is to determine strategies that provably adhere to (probabilistic) temporal logic constraints. This problem is computationally intractable and theoretically hard. We propose a novel method that combines techniques from machine learning and formal verification. First, we train a recurrent neural network (RNN) to encode POMDP strategies. The RNN accounts for memory-based decisions without the need to expand the full belief space of a POMDP. Secondly, we restrict the RNN-based strategy
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Reports on the topic "Machine learning for information improvement"

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Shead, Timothy, Jonathan Berry, Cynthia Phillips, and Jared Saia. Information-Theoretically Secure Distributed Machine Learning. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1763277.

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Li, Eliot, Charles Nicholas, Tim Oates, and Raman K. Mehra. Intelligent Record Linkage Techniques Based on Information Retrieval, Natural Language Processing, and Machine Learning. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada408937.

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Duersch, Jed, Thomas Catanach, and Ming Gu. CIS-LDRD Project 218313 Final Technical Report. Parsimonious Inference Information-Theoretic Foundations for a Complete Theory of Machine Learning. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1668936.

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Cilliers, Jacobus, Eric Dunford, and James Habyarimana. What Do Local Government Education Managers Do to Boost Learning Outcomes? Research on Improving Systems of Education (RISE), 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/064.

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Decentralization reforms have shifted responsibility for public service delivery to local government, yet little is known about how their management practices or behavior shape performance. We conducted a comprehensive management survey of mid-level education bureaucrats and their staff in every district in Tanzania, and employ flexible machine learning techniques to identify important management practices associated with learning outcomes. We find that management practices explain 10 percent of variation in a district's exam performance. The three management practices most predictive of perfo
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Hodgdon, Taylor, Anthony Fuentes, Jason Olivier, Brian Quinn, and Sally Shoop. Automated terrain classification for vehicle mobility in off-road conditions. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40219.

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The U.S. Army is increasingly interested in autonomous vehicle operations, including off-road autonomous ground maneuver. Unlike on-road, off-road terrain can vary drastically, especially with the effects of seasonality. As such, vehicles operating in off-road environments need to be in-formed about the changing terrain prior to departure or en route for successful maneuver to the mission end point. The purpose of this report is to assess machine learning algorithms used on various remotely sensed datasets to see which combinations are useful for identifying different terrain. The study collec
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Salter, R., Quyen Dong, Cody Coleman, et al. Data Lake Ecosystem Workflow. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40203.

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The Engineer Research and Development Center, Information Technology Laboratory’s (ERDC-ITL’s) Big Data Analytics team specializes in the analysis of large-scale datasets with capabilities across four research areas that require vast amounts of data to inform and drive analysis: large-scale data governance, deep learning and machine learning, natural language processing, and automated data labeling. Unfortunately, data transfer between government organizations is a complex and time-consuming process requiring coordination of multiple parties across multiple offices and organizations. Past succ
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