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Relevant bibliographies by topics / Absence of training data

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Academic literature on the topic 'Absence of training data'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Absence of training data"

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Böge, M., D. Bulatov, D. Debroize, G. Häufel, and L. Lucks. "EFFICIENT TRAINING DATA GENERATION BY CLUSTERING-BASED CLASSIFICATION." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 179–86. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-179-2022.

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Abstract. Insufficient amount or complete absence of reference data for the training of classifiers is a general topic. Especially the state-of-the-art deep learning approaches have to deal with the availability or adaption of this reference data to produce the reliable results they are designed for. This paper will pursue different approaches according to the absence of training data for land cover classification from aerial images. First, we will analyze the performance of traditional classification in the absence of reference data using clustering techniques and salient features for the assignment of semantic labels. Second, we will transfer the results as training data to a DeepLabv3+ CNN with pre-trained weights to demonstrate the usability of the generated training data. Third, we expand the clustering approaches and combine them with a Random Forest classifier. Finally, if user interaction and manual annotation of training data are still necessary, we also introduce our labeling GUI that enables a simple, fast, and comfortable training data generation with only a few clicks. To evaluate our procedure, we used two datasets, including the Vaihingen benchmark, for which ground truth is available. Without any interactive steps except setting a few algorithm paremeters, we achieved an overall accuracy of 75% using the Deeplab method with image data only.

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Spina, Robert J., Timothy E. Meyer, Linda R. Peterson, Dennis T. Villareal, Morton R. Rinder, and Ali A. Ehsani. "Absence of left ventricular and arterial adaptations to exercise in octogenarians." Journal of Applied Physiology 97, no. 5 (November 2004): 1654–59. http://dx.doi.org/10.1152/japplphysiol.01303.2003.

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Recent evidence suggests that octogenarians exhibit attenuated adaptations to training with a small increase in peak O2 consumption (V̇o2) that is mediated by a modest improvement in cardiac output without an increase in arteriovenous O2 content difference. This study was designed to determine whether diminished increases in peak V̇o2 and cardiac output in the octogenarians are associated with absence of left ventricular and arterial adaptations to exercise training. We studied 22 octogenarians (81.9 ± 3.7 yr, mean ± SD) randomly assigned a group that exercised at an intensity of 82.5 ± 5% of peak heart rate for 9 mo and 14 (age 83.1 ± 4.1) assigned to a control group. Peak V̇o2 increased 12% in the exercise group but decreased slightly (−7%) in the controls. The exercise group demonstrated significant but small decreases in the heart rate (6%, P = 0.002) and the rate-pressure product (9%, P = 0.004) during submaximal exercise at an absolute work rate. Training induced no significant changes in the left ventricular size, geometry (wall thickness-to-radius ratio), mass, and function assessed with two-dimensional echocardiography or in arterial stiffness evaluated with applanation tonometry. Data suggest that the absence of cardiac and arterial adaptations may in part account for the limited gain in aerobic capacity in response to training in the octogenarians.

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Ehsan, Shah Md Azimul, and Zaki Imam. "Analyzing the Effectiveness of Training Programs of BCS Administration Academy: Prospects and Challenges." Journal of Public Administration and Governance 10, no. 4 (November 11, 2020): 73. http://dx.doi.org/10.5296/jpag.v10i4.17740.

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The specific objective of this study was to analyze the effectiveness and the challenges of the training programs imparted by BCSAA, a leading training institution for the BCS administration cadre officials in Bangladesh. The study is qualitative is nature that used data from both primary and secondary sources. Primary data was obtained by interviewing a total of 75 participants from 113th, 114th and 115th Law and Administration Course (LAC) using semi-structured questionnaire and phone in interview. While secondary data was obtained adopting content analysis technique through reviewing books, journal articles, e-resources, unpublished monographs and newspapers. Analyzing the data from both the sources, the study has found that the training program of LAC has been effective to a certain extent as it has helped its participants to achieve some core qualities relevant to their job performance. The findings of the study also suggest that due to the intervention of this training, trainees have improved their knowhow about land management, conducting mobile courts, magisterial duties, e-filing, e-mutation etc. Furthermore, the training has resulted in significant improvement of their communication skills particularly in English, presentation skills, public speaking and in few other areas of their professional needs. However, the respondents raised few concerns about certain aspects of the training which include absence of proper need assessment for training, heavy reliance on guest speakers, huge syllabus compared to the course duration, absence of e-library facilities in the academy, sessions being mostly lecture based, absence of refreshers training etc. Nonetheless, this study has come up with certain pragmatic recommendations drawing on the narratives from the respondents. BCSAA need to address these issues with utmost priority for making this training further effective.

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Somadi, Somadi, and Indani Zulfah. "Analisis Human Error Pada Surat Penyerahan Petikemas (SP2) Dan Warehouse Management System (WMS)." Competitive 16, no. 1 (July 27, 2021): 42–51. http://dx.doi.org/10.36618/competitive.v16i1.1175.

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PT. XYZ is a logistics service company engaged in the shipping service business of sea / air ships (EMKL / EMKU) and warehousing / warehousing. In the operational field of Full Container Load (FCL) at PT XYZ, there are problems, namely Human Error (Human Error) inputting data into the Container Handover Letter (SP2) and Warehouse Management System (WMS) documents. The purpose of this study was to determine the causative factors and to find out the right recommendations for improvement in the event of data input errors in the WMS system and data input errors in the manufacture of SP2. Data collection techniques, namely by observation and interviews. The analysis technique used fishbone diagrams and 5W + 1H analysis. Factors that cause Human Error are lack of training or trainning, the number of other tasks outside the job description, the absence of work shifts, the atmosphere is too busy by EMKL who takes care of documents, and the absence of an RFID Reader. To minimize the occurrence of Human Errors is to hold training and provide motivation so that employees can take part in training in operating the WMS system, socializing and providing additional benefits or money to employees, recruiting new employees at least divided into 2 team work shifts for the WMS operation section, adding service into two parts, will reduce EMKL queues and speed up document input to WMS, conduct socialization to use RFID.

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He, Chaoyang, Emir Ceyani, Keshav Balasubramanian, Murali Annavaram, and Salman Avestimehr. "SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6865–73. http://dx.doi.org/10.1609/aaai.v36i6.20643.

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Graph Neural Networks (GNNs) are the first choice methods for graph machine learning problems thanks to their ability to learn state-of-the-art level representations from graph-structured data. However, centralizing a massive amount of real-world graph data for GNN training is prohibitive due to user-side privacy concerns, regulation restrictions, and commercial competition. Federated Learning is the de-facto standard for collaborative training of machine learning models over many distributed edge devices without the need for centralization. Nevertheless, training graph neural networks in a federated setting is vaguely defined and brings statistical and systems challenges. This work proposes SpreadGNN, a novel multi-task federated training framework capable of operating in the presence of partial labels and absence of a central server for the first time in the literature. We provide convergence guarantees and empirically demonstrate the efficacy of our framework on a variety of non-I.I.D. distributed graph-level molecular property prediction datasets with partial labels. Our results show that SpreadGNN outperforms GNN models trained over a central server-dependent federated learning system, even in constrained topologies.

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Kulikovsvkikh, I. M. "Reducing computational costs in deep learning on almost linearly separable training data." Computer Optics 44, no. 2 (April 2020): 282–89. http://dx.doi.org/10.18287/2412-6179-co-645.

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Previous research in deep learning indicates that iterations of the gradient descent, over separable data converge toward the L2 maximum margin solution. Even in the absence of explicit regularization, the decision boundary still changes even if the classification error on training is equal to zero. This feature of the so-called “implicit regularization” allows gradient methods to use more aggressive learning rates that result in substantial computational savings. However, even if the gradient descent method generalizes well, going toward the optimal solution, the rate of convergence to this solution is much slower than the rate of convergence of a loss function itself with a fixed step size. The present study puts forward the generalized logistic loss function that involves the optimization of hyperparameters, which results in a faster convergence rate while keeping the same regret bound as the gradient descent method. The results of computational experiments on MNIST and Fashion MNIST benchmark datasets for image classification proved the viability of the proposed approach to reducing computational costs and outlined directions for future research.

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Chen, Mantun, Yongjun Wang, Zhiquan Qin, and Xiatian Zhu. "Few-Shot Website Fingerprinting Attack with Data Augmentation." Security and Communication Networks 2021 (September 15, 2021): 1–13. http://dx.doi.org/10.1155/2021/2840289.

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This work introduces a novel data augmentation method for few-shot website fingerprinting (WF) attack where only a handful of training samples per website are available for deep learning model optimization. Moving beyond earlier WF methods relying on manually-engineered feature representations, more advanced deep learning alternatives demonstrate that learning feature representations automatically from training data is superior. Nonetheless, this advantage is subject to an unrealistic assumption that there exist many training samples per website, which otherwise will disappear. To address this, we introduce a model-agnostic, efficient, and harmonious data augmentation (HDA) method that can improve deep WF attacking methods significantly. HDA involves both intrasample and intersample data transformations that can be used in a harmonious manner to expand a tiny training dataset to an arbitrarily large collection, therefore effectively and explicitly addressing the intrinsic data scarcity problem. We conducted expensive experiments to validate our HDA for boosting state-of-the-art deep learning WF attack models in both closed-world and open-world attacking scenarios, at absence and presence of strong defense. For instance, in the more challenging and realistic evaluation scenario with WTF-PAD-based defense, our HDA method surpasses the previous state-of-the-art results by nearly 3% in classification accuracy in the 20-shot learning case. An earlier version of this work Chen et al. (2021) has been presented as preprint in ArXiv (https://arxiv.org/abs/2101.10063).

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Goldsmith, Rochelle L., David K. Spierer, Lana Tsao, Richard Stein, and Stuart D. Katz. "THE ABSENCE OF GAS EXCHANGE DATA RESULTS IN THE OVERESTIMATION OF THE TRAINING ZONE IN CHF PATIENTS 12." Journal of Cardiopulmonary Rehabilitation 18, no. 5 (September 1998): 349. http://dx.doi.org/10.1097/00008483-199809000-00016.

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Bochkova, E. V., E. A. Avdeeva, and A. V. Usol’tsev. "IS DATA SCIENTIST A PROFESSION OF THE PRESENT OR THE FUTURE?" Scientific Review: Theory and Practice 10, no. 7 (July 30, 2020): 1399–407. http://dx.doi.org/10.35679/2226-0226-2020-10-7-1399-1407.

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Modern society is characterized by rapid increasing the information volume and increasing its role in various fields of activity: economic, political, social and public ones. The rapid spread and introduction of information technologies, the growth of information data flow pose new challenges to the modern society. Global informatization dictates the need to form an infrastructure for processing and managing Big Data - an array of data, the processing of which requires special skills, knowledge and applications. The formation of the digital economy through the penetration of ICT and the digitalization of production processes contributes to the modernization of traditional industries, trade and procurement procedures, related financial and logistics operations, as well as markets with other sources of added value. Big Data as a new tool for analytics, forecasting and management decision making is becoming a leading asset and resource of the state, business and society as a whole. At the same time, the absence of physical boundaries in the digital space opens up access to big data for all participants in the global space. The national program “Digital Economy of the Russian Federation” is aimed at creating the necessary institutional and infrastructural conditions for the development of high-tech businesses, increasing the competitiveness of the national economy, the quality of life of citizens, and ensuring economic growth. One of the federal projects “Human Resources for the Digital Economy” is aimed at ensuring the training of highly qualified personnel for a modern society with high professional qualifications, skills and abilities to develop and introduce competitive products. Therefore, a Data Science specialist is a profession shaped by the needs of the Russian economy. In this regard, new challenges and methodologies for training IT specialists with the necessary knowledge and skills that meet the requirements of high-tech business are being formed.

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Bouhamed, Omar, Manar Amayri, and Nizar Bouguila. "Weakly Supervised Occupancy Prediction Using Training Data Collected via Interactive Learning." Sensors 22, no. 9 (April 21, 2022): 3186. http://dx.doi.org/10.3390/s22093186.

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Accurate and timely occupancy prediction has the potential to improve the efficiency of energy management systems in smart buildings. Occupancy prediction heavily depends on historical occupancy-related data collected from various sensor sources. Unfortunately, a major problem in that context is the difficulty to collect training data. This situation inspired us to rethink the occupancy prediction problem, proposing the use of an original principled approach based on occupancy estimation via interactive learning to collect the needed training data. Following that, the collected data, along with various features, were fed into several algorithms to predict future occupancy. This paper mainly proposes a weakly supervised occupancy prediction framework based on office sensor readings and occupancy estimations derived from an interactive learning approach. Two studies are the main emphasis of this paper. The first is the prediction of three occupancy states, referred to as discrete states: absence, presence of one occupant, and presence of more than one occupant. The purpose of the second study is to anticipate the future number of occupants, i.e., continuous states. Extensive simulations were run to demonstrate the merits of the proposed prediction framework’s performance and to validate the interactive learning-based approach’s ability to contribute to the achievement of effective occupancy prediction. The results reveal that LightGBM, a machine learning model, is a better fit for short-term predictions than known recursive neural networks when dealing with a limited dataset. For a 24 h window forecast, LightGBM improved accuracy from 38% to 50%, which is an excellent result for non-aggregated data (single office).

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Dissertations / Theses on the topic "Absence of training data"

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Boxwell, Stephen Arthur. "A CCG-Based Method for Training a Semantic Role Labeler in the Absence of Explicit Syntactic Training Data." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1322594816.

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Whaley, Steven R. J. "Bayesian analysis of sickness absence data." Thesis, University of Aberdeen, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274884.

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Sickness-absence (SA) is a serious financial burden to UK industry totalling £10-12 billion in 1999 the equivalent of £434 and 7.8 days lost per worker. A major change in the reporting of SA occurred on 14 June 1982 with the introduction of self certification. Up to then all episodes had to be certified by a general practitioner. Since then, events that lasted for seven calendar or less have not required a GP's certificate and are 'self-certified'. A SA episode consists of the date the individual went off sick, the duration of the episode and a medical diagnosis given by either a GP or self diagnosis. A common approach to the analysis of SA data is to model the number of times an individual went off sick during a period of follow up via Poisson regression. Some studies on SA have examined the duration of SA, though most concentrated on the probability of going off sick. This thesis uses an intensity based approach to model the joint probability that a person goes off sick with a specific disease and has a specific duration of absence (the 'joint analysis'). A Bayesian hierarchical model, based on the conditional proportional hazards model, is formulated for the joint analysis and sampled using Markov chain Monte Carlo methods. Posterior expectations and 90% credible intervals are presented as summaries of the marginal posterior distributions of the parameters of the joint analysis. Trace plots of the log-joint posterior distribution are given to assess convergence of the MCMC sampler.

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Yousefi, Sepehr. "Credit Risk Management in Absence of Financial and Market Data." Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188800.

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Credit risk management is a significant fragment in financial institutions' security precautions against the downside of their investments. A major quandary within the subject of credit risk is the modeling of simultaneous defaults. Globalization causes economises to be affected by innumerous external factors and companies to become interdependent, which in turn enlarges the complexity of establishing reliable mathematical models. The precarious situation is exacerbated by the fact that managers often suffer from the lack of data. The default correlations are most often calibrated by either using financial and/or market information. However, there exists circumstances where these types of data are inaccessible or unreliable. The problem of scarce data also induces diculties in the estimation of default probabilities. The frequency of insolvencies and changes in credit ratings are usually updated on an annual basis and historical information covers 20-25 years at best. From a mathematical perspective, this is considered as a small sample and standard statistical models are inferior in such situations. The first part of this thesis specifies the so-called entropy model which estimates the impact of macroeconomic fluctuations on the probability of defaults, and aims to outperform standard statistical models for small samples. The second part specifies the CIMDO, a framework for modeling correlated defaults without financial and market data. The last part submits a risk analysis framework for calculating the uncertainty in the simulated losses. It is shown that the entropy model will reduce the variance of the regression coefficients but increase its bias compared to the OLS and Maximum Likelihood. Furthermore there is a significant difference between the Student's t CIMDO and the t-Copula. The former appear to reduce the model uncertainty, however not to such extent that evident conclusions were carried out.
Kreditriskhantering är den enskilt viktigaste delen i banker och finansiella instituts säkerhetsåtgärder mot nedsidor i deras investeringar. En påtaglig svårighet inom ämnet är modelleringen av simultana konkurser. Globalisering ökar antalet parametrar som påverkar samhällsekonomin, vilket i sin tur försvårar etablering av tillförlitliga matematiska modeller. Den prekära situationen förvärras av det faktum att analytiker genomgående saknar tillräcklig data. Konkurskorrelation är allt som oftast kalibrerad med hjälp av information från årsrapporter eller marknaden. Dessvärre existerar det omständigheter där sådana typer av data är otillgängliga eller otillförlitliga. Samma problematik skapar även svårigheter i skattningen av sannolikheten till konkurs. Uppgifter såsom frekvensen av insolventa företag eller förändringar i kreditbetyg uppdateras i regel årligen, och historisk data täcker i bästa fall 20-25 år. Syftet med detta examensarbete är att ge ett övergripande ramverk för kreditriskhantering i avsaknad av finansiell information och marknadsdata. Detta innefattar att estimera vilken påverkan fluktueringar i makroekonomin har på sannolikheten för konkurs, modellera korrelerade konkurser samt sammanfatta ett ramverk för beräkning av osäkerheten i den estimerade förlustdistributionen. Den första delen av examensarbetet specificerar den så kallade entropy modellen. Denna skattar påverkan av makroekonomin på sannolikheterna för konkurs och ämnar att överträffa statistiska standardmodeller vid små datamängder. Den andra delen specificerar CIMDO, ett ramverk för beräkning av konkurskorrelation när marknads- och företagsdata saknas. Den sista delen framlägger ett ramverk för riskanalys av förlustdistributionen. Det visas att entropy modellen reducerar variansen i regressionskoefficienter men till kostnad av att försämra dess bias. Vidare är det en signifikant skillnad mellan student’s t CIMDO och t-Copula. Det förefaller som om den förstnämnda reducerar osäkerheten i beräkningarna, men inte till den grad att uppenbara slutsatser kan dras.

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Nilsson, Maria. "Differences and similarities in work absence behavior : - empirical evidence from micro data." Doctoral thesis, Växjö universitet, Ekonomihögskolan, EHV, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-626.

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This thesis consists of three self-contained essays about absenteeism. Essay I analyzes if the design of the insurance system affects work absence, i.e. the classic insurance problem of moral hazard. Several reforms of the sickness insurance system were implemented during the period 1991-1996. Using Negative binomial models with fixed effects, the analysis show that both workers and employers changed their behavior due to the reforms. We also find that the extent of moral hazard varies depending on work contract structures. The reforms reducing the compensation levels decreased workers’ absence, both the number of absent days and the number of absence spells. The reform in 1992, introducing sick pay paid by the employers, also decreased absence levels, which probably can be explained by changes in personnel policy such as increased use of monitoring and screening of workers. Essay II examines the background to gender differences in work absence. Women are found, as in many earlier studies, to have higher absence levels than men. Our analysis, using finite mixture models, reveals that there are a group of women, comprised of about 41% of the women in our sample, that have a high average demand of absence. Among men, the high demand group is smaller consisting of about 36% of the male sample. The absence behavior differs as much between groups within gender as it does between men and women. The access to panel data covering the period 1971-1991 enables an analysis of the increased gender gap over time. Our analysis shows that the increased gender gap can be attributed to changes in behavior rather than in observable characteristics. Essay III analyzes the difference in work absence between natives and immigrants. Immigrants are found to have higher absence than natives when measured as the number of absent days. For the number of absence spells, the pattern for immigrants and natives is about the same. The analysis, using panel data and count data models, show that natives and immigrants have different characteristics concerning family situation, work conditions and health. We also find that natives and immigrants respond differently to these characteristics. We find, for example, that the absence of natives and immigrants are differently related to both economic incentives and work environment. Finally, our analysis shows that differences in work conditions and work environment only can explain a minor part of the ethnic differences in absence during the 1980’s.

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Johnston, Cristin D. "Observation training evaluating a procedure for generating self-rules in the absence of reinforcement /." abstract and full text PDF (UNR users only), 2008. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3316373.

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Muhammad, Azhar Ranjha, and Adnan Ghalib Ahmad. "Data Analysis and Graph Presentation of Team Training Data." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-66998.

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This Report illustrates the team training system presentation as a web based graphs.The research is done based on the presentation of web information stored in database into the graphicalform. Ice-Faces with SQL database at back end data source is the way to demonstrate the implementationof graph system. By having research and comparisons it is found suitably best the Graph generating systemfor analysis of C3fire records.Several models for graphs are been selected for the illustration of best visualization of the demography andat last one with best demonstration of result is selected.The information which was displayed in tables stored in database is now viewable in the graphical format.The implementation was done by modifying and embedding codes in the previous version and successfullyimplementation is done. The graphs are displayed by the values stored in database and dynamicallyupdated as the values in the database are changed. There are four graphs finally selected and implementedthat shows the data, which are pie, bar, line and cluster bar graphs representing data in best viewableform.
C3Fire

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Powers, Richard. "Track-loss detection in the absence of truth data for target tracking in clutter." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3273736.

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Goulette, Dana E. "Training assessment and modeling subjective data encapsulation for the National Training Center." Monterey, California. Naval Postgraduate School, 1997. http://hdl.handle.net/10945/9084.

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Approved for public release; distribution is unlimited
The National Training Center (NTC) located at Fort Irwin, California performs the critical Army mission of preparing battalion task forces and brigade staffs for combat. The NTC provides a unique opportunity to assess training proficiency. To assist in the training assessment of rotating units, the Army has spent millions of dollars on a state of the art instrumentation system that transmits objective data from all player vehicles and stores the information in a database. Currently, no subjective observer-controller (O/C) observations of training are stored in the database. The primary emphasis of this research is to develop a training assessment system and model subjective data encapsulation to enhance training performance analysis. The assessment system is designed to be incorporated into a relational database that will allow analysis of various measures of performance that provide input for platoon through brigade level After Action Reviews (AAR). Additionally, the database will support methods for simple data manipulation for the purpose of conducting post-rotation analysis and the identification of trends

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Chang, Eric I.-Chao. "Improving wordspotting performance with limited training data." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/38056.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
Includes bibliographical references (leaves 149-155).
by Eric I-Chao Chang.
Ph.D.

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Georgsson, Adam, and Olof Christensson. "Visualization of training data reportedby football players." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16694.

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Background. Data from training sessions is gathered by a trainer from the playerswith the goal of analyzing and getting an overview of how the team is performing.The collected data is represented in tabular form, and over time the effort to inter-pret it becomes more demanding. Objectives. This thesis’ goal is to find out if there is a solution where collecting,processing and representing training data from football players can ease and improvethe trainer’s analysis of the team. Methods. A dataset is received from a football trainer, and it contains informa-tion about training sessions for his team of football players. The dataset is used tofind a suitable method and visualize the data. Feedback from the trainer is used todetermine what works and what does not. Furthermore, a survey with examples ofvisualization is given to the players and the trainer to get an understanding of howthe selected charts are interpreted. Results. Representing the attributes of most importance from received datasetrequires a chain of views (usage flow) to be introduced, from primary view to qua-ternary view. Each step in the chain tightens the level of details represented. Boxplot proved to be an appropriate choice to provide an overview of the team’s trainingdata. Conclusions. Visualizing training data gives a significant advantage to the trainerregarding team analysis. With box plotting will the trainer get an overview of theteam and can hereafter dig into more detailed data while interacting with the charts

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Books on the topic "Absence of training data"

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Chakrabarty, Dalia. Learning in the Absence of Training Data. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-31011-9.

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Services, CHT Training, ed. Managing absence: Training pack. Liverpool: CHT Training Solutions, 1997.

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Central Council for Education and Training in Social Work. Data on training. London: The Council, 1989.

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Central Council for Education and Training in Social Work. Data on training. London: The Council, 1986.

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Rowland, John L. Data systems technician training series. [Pensacola, Fla.]: The Activity, 1992.

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Maynard, Robert M. Data systems technician training series. [Pensacola, Fla.]: The Activity, 1996.

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Occupational projections and training data. 2nd ed. Washington, D.C: U.S. Dept. of Labor. Bureau of Labor Statistics, 2004.

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Inc, PCI. Training data set: Version 5.3. Richmond Hill, Ont: PCI, 1994.

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Maynard, Robert M. Data systems technician training series. [Pensacola, Fla.]: The Activity, 1996.

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Maynard, Robert M. Data systems technician training series. [Pensacola, Fla.]: The Activity, 1994.

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Book chapters on the topic "Absence of training data"

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Chakrabarty, Dalia. "Bespoke Learning to Generate Originally-Absent Training Data." In Learning in the Absence of Training Data, 1–22. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-31011-9_1.

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Chakrabarty, Dalia. "Learning the Temporally-Evolving Evolution-Driving Function of a Dynamical System, to Forecast Future States: Forecasting New COVID19 Infection Numbers." In Learning in the Absence of Training Data, 23–100. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-31011-9_2.

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Chakrabarty, Dalia. "Potential to Density via Poisson Equation: Application to Bespoke Learning of Gravitational Mass Density in Real Galaxy." In Learning in the Absence of Training Data, 101–51. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-31011-9_3.

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Chakrabarty, Dalia. "Bespoke Learning of Disease Progression Using Inter-Network Distance: Application to Haematology-Oncology: Joint Work with Dr. Kangrui Wang, Dr. Akash Bhojgaria and Dr. Joydeep Chakrabartty." In Learning in the Absence of Training Data, 189–217. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-31011-9_5.

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Chakrabarty, Dalia. "Bespoke Learning in Static Systems: Application to Learning Sub-surface Material Density Function." In Learning in the Absence of Training Data, 153–88. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-31011-9_4.

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Spire, Cedric, and Dalia Chakrabarty. "Learning in the Absence of Training Data—A Galactic Application." In Springer Proceedings in Mathematics & Statistics, 43–51. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30611-3_5.

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Khan, Shehroz S., Michelle E. Karg, Dana Kulić, and Jesse Hoey. "X-Factor HMMs for Detecting Falls in the Absence of Fall-Specific Training Data." In Ambient Assisted Living and Daily Activities, 1–9. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13105-4_1.

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Bacci, Silvia, Bruno Bertaccini, Riccardo Bruni, Federico Crescenzi, and Beatrice Donati. "A structural equation model to measure logical competences." In Proceedings e report, 37–41. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.08.

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Logical abilities are a ubiquitous ingredient in all those contexts that take into account soft skills, argumentative skills or critical thinking. However, the relationship between logical models and the enhancement of these abilities is rarely explicitly considered. Two aspects of the issue are particularly critical in our opinion, namely: (i) the lack of statistically relevant data concerning these competences; (ii) the absence of reliable indices that might be used to detect and measure the possession of abilities underlying the aforementioned skills. This paper addresses both aspects of this topic by presenting the results of a research that we conducted between October and December 2020 on students enrolled in various degree courses at the University of Florence. The dataset has been collected by a three-stage initiative. We started from an entrance examination to assess the students' initial abilities. This test included ten questions, each regarding a specific aspect of logical reasoning. Then, students underwent a short training course to strengthen their knowledge of logic. At the end of the short course, students underwent a final examination to evaluate the effectiveness of the training. To verify the effectiveness of the training program we used students’ characteristics into a Structural Equation Model comparing the distribution of abilities before and after the training.

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Bertaccini, Bruno, Riccardo Bruni, Federico Crescenzi, and Beatrice Donati. "Measuring logical competences and soft skills when enrolling in a university degree course." In Proceedings e report, 41–46. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.09.

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Logical abilities are a ubiquitous ingredient in all those contexts that take into account soft skills, argumentative skills or critical thinking. However, the relationship between logical models and the enhancement of these abilities is rarely explicitly considered. Two aspects of the issue are particularly critical in our opinion, namely: (i) the lack of statistically relevant data concerning these competences; (ii) the absence of reliable indices that might be used to measure and detect the possession of abilities underlying the above-mentioned soft skills. This paper aims to address both aspects of this topic by presenting the results of a research we conducted in the period October – December 2020 on students enrolled in various degree courses at the University of Florence. To the best of our knowledge, to date this is the largest available database on the subject in the Italian University System. It has been obtained by a three-stage initiative. We started from an “entrance” examination for assessing the students' initial abilities. This test comprised ten questions, each of which was centered on a specific reasoning construct. The results we have collected show that there is a widespread lack of understanding of basic patterns that are common in the everyday way of arguing. Students then underwent a short training course, using formal logic techniques in order to strengthen their abilities, and afterwards took an “exit” examination, replicating the structure and the questions difficulty of the entrance one in order to evaluate the effectiveness of the course. Results show that the training was beneficial.

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Uther, William, Dunja Mladenić, Massimiliano Ciaramita, Bettina Berendt, Aleksander Kołcz, Marko Grobelnik, Dunja Mladenić, et al. "Training Data." In Encyclopedia of Machine Learning, 989. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_840.

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Conference papers on the topic "Absence of training data"

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Cagliero, Luca, and Moreno La Quatra. "Automatic slides generation in the absence of training data." In 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2021. http://dx.doi.org/10.1109/compsac51774.2021.00025.

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Chakraborty, Anirban, Kripabandhu Ghosh, and Swapan Kumar Parui. "Retrieval from Noisy E-Discovery Corpus in the Absence of Training Data." In SIGIR '15: The 38th International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2766462.2767828.

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Shi, Kang. "A method for generating various style Chinese fonts in the absence of training data (S)." In The 33rd International Conference on Software Engineering and Knowledge Engineering. KSI Research Inc., 2021. http://dx.doi.org/10.18293/seke2021-114.

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Srivastava, Jayesh, and L. H. Shu. "The Affordance of Absence." 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/detc2014-35285.

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We originally studied affordances to design products that encourage resource-efficient behavior in users. This paper was motivated by the observation that a lack of resources affected the ability of impoverished slum residents to perceive affordances. Data gathered from a trip to Bangladesh support recent postulations that affordance perception depends on user intention and socio-cultural norms, which are also influenced by fixation and training. We thus became interested in how absence, whether of material, components, or entire products, affected the utility of objects. We describe three dimensions of absence, one major and two minor. The major dimension is used to express the range of physical absence, i.e., of material in a part, of a part in a product, and of the entire product. Minor dimensions include the extent of user control and the permanence of absence, each of which can be used to increase utility in objects. We provide existing examples along these dimensions and describe concepts along the major, physical dimension of absence. We propose that the affordance of absence can be used as an approach to identify novel affordances, and conclude by placing it in the context of existing affordance-based design methods.

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Barkas, Dimitrios A., Konstantinos Kalkanis, George Ch Ioannidis, Stavros D. Kaminaris, and Constantinos S. Psomopoulos. "Simulated Partial Discharge Harmonic Data generation for Neural Network Training in the Absence of real measurements: A first Approach." In 2022 Panhellenic Conference on Electronics & Telecommunications (PACET). IEEE, 2022. http://dx.doi.org/10.1109/pacet56979.2022.9976352.

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Marhoum, Adil. "Plenary lecture: the teaching of statistics in morocco l’enseignement de la statistique au maroc." In Teaching Statistics in a Data Rich World. International Association for Statistical Education, 2017. http://dx.doi.org/10.52041/srap.17103.

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Discussing higher education in Morocco, particularly about statistics, the talk reflects on the following points: • Interest of statistics • Place of statistics in education programs in Morocco • Difficulties in teaching statistics • Teachers of statistics and teaching methods • Suggestions for further increasing the penetration of statistics into curricula Various reforms of Moroccan education have consistently placed more emphasis on the teaching of statistics. This teaching is currently being introduced in most of the training streams, such as economics, management sciences, and agronomic sciences. Despite this positive development, there are a number of shortcomings in the teaching of statistics. There is an absence of statistical reasoning in students: the ability to analyze and interpret the results of calculations. Statistics courses are taught by teachers who have often been forced to teach it, usually mathematics teachers, who on the whole have a formal approach to statistics. The mathematician takes the place of the statistician, without considering more applied questions. In order to encourage better statistical understanding, we suggest the presence of experienced statisticians in any university or scientific research institution, in association if necessary; training seminars (Study visits, refresher courses, thematic networking of statisticians, etc.) in countries with more statistics education capacity; local training for researchers; participation in regional seminars; creation of courses of study more oriented towards statistical processing and data processing; and drafting and dissemination of guides to good statistical practice in experimentation, sampling and publication of results.

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Jain, Arjit, Pranay Reddy Samala, Preethi Jyothi, Deepak Mittal, and Maneesh Singh. "Perturb, Predict & Paraphrase: Semi-Supervised Learning using Noisy Student for Image Captioning." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/105.

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Recent semi-supervised learning (SSL) methods are predominantly focused on multi-class classification tasks. Classification tasks allow for easy mixing of class labels during augmentation which does not trivially extend to structured outputs such as word sequences that appear in tasks like image captioning. Noisy Student Training is a recent SSL paradigm proposed for image classification that is an extension of self-training and teacher-student learning. In this work, we provide an in-depth analysis of the noisy student SSL framework for the task of image captioning and derive state-of-the-art results. The original algorithm relies on computationally expensive data augmentation steps that involve perturbing the raw images and computing features for each perturbed image. We show that, even in the absence of raw image augmentation, the use of simple model and feature perturbations to the input images for the student model are beneficial to SSL training. We also show how a paraphrase generator could be effectively used for label augmentation to improve the quality of pseudo labels and significantly improve performance. Our final results in the limited labeled data setting (1% of the MS-COCO labeled data) outperform previous state-of-the-art approaches by 2.5 on BLEU4 and 11.5 on CIDEr scores.

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Sudakov, O. V., and D. V. Dmitriev. "Comparison of G-Means Algorithms and Kohonen Network in Solving Clustering Problems." In 32nd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2022. http://dx.doi.org/10.20948/graphicon-2022-1147-1156.

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Purpose: In this paper, the question of how to improve a self-organizing neural network consisting of a bundle of clustering algorithm and a multilayer perceptron for data verification tasks in the absence of training pairs is considered. Design/methodology/approach: The most popular clustering algorithm is the Kohonen network, but today it is not the only algorithm capable of performing the task quickly and accurately. The paper compares the Kohonen network and the G-Means algorithm. The principle of operation of these two algorithms is briefly analyzed. The accuracy of these algorithms and the speed of their learning are compared. Findings: By conducting experiments, conclusions were drawn about the speed and accuracy of the algorithms. Originality/value: The relevance of this work lies in the fact that the preparation of training pairs for intelligent systems and the process of learning with a teacher is a resource-intensive task. The systems of self-learning algorithms under consideration will significantly increase the learning rate, as well as eliminate the need for manual classification of data and the creation of training pairs for the perceptron, which in turn will allow you to create a self-learning system with the ability to generalize and predict.

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Mao, Xudong, and Qing Li. "Unpaired Multi-Domain Image Generation via Regularized Conditional GANs." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/354.

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In this paper, we study the problem of multi-domain image generation, the goal of which is to generate pairs of corresponding images from different domains. With the recent development in generative models, image generation has achieved great progress and has been applied to various computer vision tasks. However, multi-domain image generation may not achieve the desired performance due to the difficulty of learning the correspondence of different domain images, especially when the information of paired samples is not given. To tackle this problem, we propose Regularized Conditional GAN (RegCGAN) which is capable of learning to generate corresponding images in the absence of paired training data. RegCGAN is based on the conditional GAN, and we introduce two regularizers to guide the model to learn the corresponding semantics of different domains. We evaluate the proposed model on several tasks for which paired training data is not given, including the generation of edges and photos, the generation of faces with different attributes, etc. The experimental results show that our model can successfully generate corresponding images for all these tasks, while outperforms the baseline methods. We also introduce an approach of applying RegCGAN to unsupervised domain adaptation.

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Hassan, Mohd Firdaus Bin, and Philip Bonello. "A Neural Network Identification Technique for a Foil-Air Bearing and its Application to Unbalance Response Analysis." In ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-42190.

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This paper proposes and studies the non-parametric system identification of a foil-air bearing (FAB) and its application to the frequency-domain nonlinear analysis of a foil-air bearing rotor system. This research is motivated by two advantages: (i) it removes computational limitations by replacing the air film and foil structure state equations by a displacement/force relationship; (ii) if the identification is based on empirical data, it can capture complications that cannot be easily modelled. A numerical model of the FAB is identified using a recurrent neural network (RNN). The training data sets are taken from the simultaneous time domain solution of the air film, foil and rotor equations. The RNN FAB model identified at a single speed is then validated over a range of speeds in two ways: (i) by subjecting it to several sets of input-output data that are different from those used in training; (ii) by using it in the harmonic balance (HB) solution process for the unbalance response of a rotor-bearing system. In either case, the test results using the identified model show good agreement with the exact results obtained using the air film and foil equations, demonstrating the great potential of this method, in the absence of self-excitation effects.

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Reports on the topic "Absence of training data"

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Komba, Aneth, and Richard Shukia. Accountability Relationships in 3Rs Curriculum Reform Implementation: Implication for Pupils’ Acquisition of Literacy and Numeracy Skills in Tanzania’s Primary Schools. Research on Improving Systems of Education (RISE), March 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/065.

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This study responded to one key research question: What are the accountability relationships between the actors in implementing the 3Rs curriculum reform? A qualitative research approach informed the study, using key informant interviews, focus group discussion and document review. The data were analysed using thematic and content analysis. The study established that the key actors in implementing the 3Rs curriculum are the government institutions and the development partners. These actors provide teaching, learning materials and support in the provision of in-service teacher training. Yet, the pupils’ and teachers’ materials prepared by the donor programmes were never authorised by the Commissioner for Education. The study also found that the implementation of the 3Rs was very uneven across the country, with some regions receiving support from both the government and donors, and others receiving support from the government only. Consequently, schools in areas that were exposed to more than one type of support benefited from various teaching and learning materials, which led to confusion regarding when to use them. Moreover, the initiatives by several donors exclusively focus on public schools, which use Kiswahili as the medium of instruction and hence, there existed inequality across the various types of schools. Furthermore, the funds for implementing the reform were provided by both the development partners and the government. The Global Partnership for Education (GPE)—Literacy and Numeracy Education Support (LANES) Program— provided a large proportion of the funds. However, the funds remained insufficient to meet the training needs. As a result, the training was provided for only few days and to a few teachers. Consequently, the sustainability of the reform, in the absence of donor funding, remains largely questionable.

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Hastie, Rachel, and Amy O'Donnell. Responsible Data Management training pack. Oxfam, March 2017. http://dx.doi.org/10.21201/2017.9118.

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Kemper, Toni, and Jack Briscoe. National Training Center Data Library. Fort Belvoir, VA: Defense Technical Information Center, January 1987. http://dx.doi.org/10.21236/ada178947.

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Dilks, Kelly, Jeffery Miller, Amit Patel, and Jim Cookas. Geospatial Data Computer-Based Training. Fort Belvoir, VA: Defense Technical Information Center, February 2000. http://dx.doi.org/10.21236/ada375198.

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Moranville, April, and Donald H. Hewitt. Rating Training Continuum: Baseline Data. Fort Belvoir, VA: Defense Technical Information Center, October 1992. http://dx.doi.org/10.21236/ada257078.

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Orekhvo, D. O., and R. V. Khrunichev. Remote training course "data Analysis", training direction 38.03.05 " Business Informatics". OFERNIO, June 2018. http://dx.doi.org/10.12731/ofernio.2018.23676.

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Johnson, Carol A., and Michael J. Shadell. Training and Lessons Learned Data Requirements from the National Training Center. Fort Belvoir, VA: Defense Technical Information Center, April 1988. http://dx.doi.org/10.21236/ada194327.

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Teachout, Mark S., Douglas J. Sego, and J. K. Ford. Extending the Training Efficiency and Effectiveness Methodology (TEEM) with Training Transfer Data. Fort Belvoir, VA: Defense Technical Information Center, June 1995. http://dx.doi.org/10.21236/ada303559.

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Bercos, James, and Richard C. Eakins. Extension of Training Extension Course Cost and Training Effectiveness Analysis Data Collection. Fort Belvoir, VA: Defense Technical Information Center, February 1985. http://dx.doi.org/10.21236/ada160367.

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Webb, David W. Quantal Response Analysis in the Absence of a Zone of Mixed Results Using Data Augmentation. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada491027.

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