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Academic literature on the topic 'LIMITED DATASET'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "LIMITED DATASET"

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Gusarova, Nataliya, Artem Lobantsev, Aleksandra Vatian, Anton Klochrov, Maxim Kabyshev, Anatoly Shalyto, Anna Tatarinova, Tatiana Treshkur, and Min Li. "Generative augmentation to improve lung nodules detection in resource-limited settings." Information and Control Systems, no. 6 (December 15, 2020): 60–69. http://dx.doi.org/10.31799/1684-8853-2020-6-60-69.

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Introduction: Lung cancer is one of the most formidable cancers. The use of neural networks technologies in its diagnostics is promising, but the datasets collected from real clinical practice cannot cover a variety of lung cancer manifestations. Purpose: Assessment of the possibility of improving the classification of pulmonary nodules by means of generative augmentation of available datasets under resource constraints. Methods: We used part of LIDC-IDRI dataset, the StyleGAN architecture for generating artificial lung nodules and the VGG11 model as a classifier. We generated pulmonary nodules using the proposed pipeline and invited four experts to visually evaluate them. We formed four experimental datasets with different types of augmentation, including use of synthesized data, and we compared the effectiveness of the classification performed by the VGG11 network when training for each dataset. Results: 10 generated nodules in each group of characteristics were presented for assessment. In all cases, positive expert assessments were obtained with a Fleiss's kappa coefficient k = 0.6–0.9. We got the best values of ROCAUC=0.9604 and PRAUC=0.9625 with the proposed approach of a generative augmentation. Discussion: The obtained efficience metrics are superior to the baseline results obtained using comparably small training datasets, and slightly less than the best results achieved using much more powerful computational resources. So, we have shown that one can effectively use for augmenting an unbalanced dataset a combination of StyleGAN and VGG11, which does not require large computing resources as well as a large initial dataset for training.

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Sarwati Rahayu, Sulis Sandiwarno, Erwin Dwika Putra, Marissa Utami, and Hadiguna Setiawan. "Model Sequential Resnet50 Untuk Pengenalan Tulisan Tangan Aksara Arab." JSAI (Journal Scientific and Applied Informatics) 6, no. 2 (June 30, 2023): 234–41. http://dx.doi.org/10.36085/jsai.v6i2.5379.

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Research for Arabic handwriting recognition is still limited. The number of public datasets regarding Arabic script is still limited for this type of public dataset. Therefore, each study usually uses its dataset to conduct research. However, recently public datasets have become available and become research opportunities to compare methods with the same dataset. This study aimed to determine the implementation of the transfer learning model with the best accuracy for handwriting recognition in Arabic script. The results of the experiment using ResNet50 are as follows: training accuracy is 91.63%, validation accuracy is 91.82%, and the testing accuracy is 95.03%.

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Mohammad Alfadli, Khadijah, and Alaa Omran Almagrabi. "Feature-Limited Prediction on the UCI Heart Disease Dataset." Computers, Materials & Continua 74, no. 3 (2023): 5871–83. http://dx.doi.org/10.32604/cmc.2023.033603.

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Ko, Yu-Chieh, Wei-Shiang Chen, Hung-Hsun Chen, Tsui-Kang Hsu, Ying-Chi Chen, Catherine Jui-Ling Liu, and Henry Horng-Shing Lu. "Widen the Applicability of a Convolutional Neural-Network-Assisted Glaucoma Detection Algorithm of Limited Training Images across Different Datasets." Biomedicines 10, no. 6 (June 3, 2022): 1314. http://dx.doi.org/10.3390/biomedicines10061314.

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Automated glaucoma detection using deep learning may increase the diagnostic rate of glaucoma to prevent blindness, but generalizable models are currently unavailable despite the use of huge training datasets. This study aims to evaluate the performance of a convolutional neural network (CNN) classifier trained with a limited number of high-quality fundus images in detecting glaucoma and methods to improve its performance across different datasets. A CNN classifier was constructed using EfficientNet B3 and 944 images collected from one medical center (core model) and externally validated using three datasets. The performance of the core model was compared with (1) the integrated model constructed by using all training images from the four datasets and (2) the dataset-specific model built by fine-tuning the core model with training images from the external datasets. The diagnostic accuracy of the core model was 95.62% but dropped to ranges of 52.5–80.0% on the external datasets. Dataset-specific models exhibited superior diagnostic performance on the external datasets compared to other models, with a diagnostic accuracy of 87.50–92.5%. The findings suggest that dataset-specific tuning of the core CNN classifier effectively improves its applicability across different datasets when increasing training images fails to achieve generalization.

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Guo, Runze, Bei Sun, Xiaotian Qiu, Shaojing Su, Zhen Zuo, and Peng Wu. "Fine-Grained Recognition of Surface Targets with Limited Data." Electronics 9, no. 12 (December 2, 2020): 2044. http://dx.doi.org/10.3390/electronics9122044.

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Recognition of surface targets has a vital influence on the development of military and civilian applications such as maritime rescue patrols, illegal-vessel screening, and maritime operation monitoring. However, owing to the interference of visual similarity and environmental variations and the lack of high-quality datasets, accurate recognition of surface targets has always been a challenging task. In this paper, we introduce a multi-attention residual model based on deep learning methods, in which channel and spatial attention modules are applied for feature fusion. In addition, we use transfer learning to improve the feature expression capabilities of the model under conditions of limited data. A function based on metric learning is adopted to increase the distance between different classes. Finally, a dataset with eight types of surface targets is established. Comparative experiments on our self-built dataset show that the proposed method focuses more on discriminative regions, avoiding problems like gradient disappearance, and achieves better classification results than B-CNN, RA-CNN, MAMC, and MA-CNN, DFL-CNN.

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Gaikwad, Mayur, Swati Ahirrao, Shraddha Phansalkar, Ketan Kotecha, and Shalli Rani. "Multi-Ideology, Multiclass Online Extremism Dataset, and Its Evaluation Using Machine Learning." Computational Intelligence and Neuroscience 2023 (March 1, 2023): 1–33. http://dx.doi.org/10.1155/2023/4563145.

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Social media platforms play a key role in fostering the outreach of extremism by influencing the views, opinions, and perceptions of people. These platforms are increasingly exploited by extremist elements for spreading propaganda, radicalizing, and recruiting youth. Hence, research on extremism detection on social media platforms is essential to curb its influence and ill effects. A study of existing literature on extremism detection reveals that it is restricted to a specific ideology, binary classification with limited insights on extremism text, and manual data validation methods to check data quality. In existing research studies, researchers have used datasets limited to a single ideology. As a result, they face serious issues such as class imbalance, limited insights with class labels, and a lack of automated data validation methods. A major contribution of this work is a balanced extremism text dataset, versatile with multiple ideologies verified by robust data validation methods for classifying extremism text into popular extremism types such as propaganda, radicalization, and recruitment. The presented extremism text dataset is a generalization of multiple ideologies such as the standard ISIS dataset, GAB White Supremacist dataset, and recent Twitter tweets on ISIS and white supremacist ideology. The dataset is analyzed to extract features for the three focused classes in extremism with TF-IDF unigram, bigrams, and trigrams features. Additionally, pretrained word2vec features are used for semantic analysis. The extracted features in the proposed dataset are evaluated using machine learning classification algorithms such as multinomial Naïve Bayes, support vector machine, random forest, and XGBoost algorithms. The best results were achieved by support vector machine using the TF-IDF unigram model confirming 0.67 F1 score. The proposed multi-ideology and multiclass dataset shows comparable performance to the existing datasets limited to single ideology and binary labels.

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Huč, Aleks, Jakob Šalej, and Mira Trebar. "Analysis of Machine Learning Algorithms for Anomaly Detection on Edge Devices." Sensors 21, no. 14 (July 20, 2021): 4946. http://dx.doi.org/10.3390/s21144946.

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The Internet of Things (IoT) consists of small devices or a network of sensors, which permanently generate huge amounts of data. Usually, they have limited resources, either computing power or memory, which means that raw data are transferred to central systems or the cloud for analysis. Lately, the idea of moving intelligence to the IoT is becoming feasible, with machine learning (ML) moved to edge devices. The aim of this study is to provide an experimental analysis of processing a large imbalanced dataset (DS2OS), split into a training dataset (80%) and a test dataset (20%). The training dataset was reduced by randomly selecting a smaller number of samples to create new datasets Di (i = 1, 2, 5, 10, 15, 20, 40, 60, 80%). Afterwards, they were used with several machine learning algorithms to identify the size at which the performance metrics show saturation and classification results stop improving with an F1 score equal to 0.95 or higher, which happened at 20% of the training dataset. Further on, two solutions for the reduction of the number of samples to provide a balanced dataset are given. In the first, datasets DRi consist of all anomalous samples in seven classes and a reduced majority class (‘NL’) with i = 0.1, 0.2, 0.5, 1, 2, 5, 10, 15, 20 percent of randomly selected samples. In the second, datasets DCi are generated from the representative samples determined with clustering from the training dataset. All three dataset reduction methods showed comparable performance results. Further evaluation of training times and memory usage on Raspberry Pi 4 shows a possibility to run ML algorithms with limited sized datasets on edge devices.

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Muniraj, Inbarasan, Changliang Guo, Ra'ed Malallah, Harsha Vardhan R. Maraka, James P. Ryle, and John T. Sheridan. "Subpixel based defocused points removal in photon-limited volumetric dataset." Optics Communications 387 (March 2017): 196–201. http://dx.doi.org/10.1016/j.optcom.2016.11.047.

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Shin, Changho, Seungeun Rho, Hyoseop Lee, and Wonjong Rhee. "Data Requirements for Applying Machine Learning to Energy Disaggregation." Energies 12, no. 9 (May 5, 2019): 1696. http://dx.doi.org/10.3390/en12091696.

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Energy disaggregation, or nonintrusive load monitoring (NILM), is a technology for separating a household’s aggregate electricity consumption information. Although this technology was developed in 1992, its practical usage and mass deployment have been rather limited, possibly because the commonly used datasets are not adequate for NILM research. In this study, we report the findings from a newly collected dataset that contains 10 Hz sampling data for 58 houses. The dataset not only contains the aggregate measurements, but also individual appliance measurements for three types of appliances. By applying three classification algorithms (vanilla DNN (Deep Neural Network), ML (Machine Learning) with feature engineering, and CNN (Convolutional Neural Network) with hyper-parameter tuning) and a recent regression algorithm (Subtask Gated Network) to the new dataset, we show that NILM performance can be significantly limited when the data sampling rate is too low or when the number of distinct houses in the dataset is too small. The well-known NILM datasets that are popular in the research community do not meet these requirements. Our results indicate that higher quality datasets should be used to expedite the progress of NILM research.

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Althnian, Alhanoof, Duaa AlSaeed, Heyam Al-Baity, Amani Samha, Alanoud Bin Dris, Najla Alzakari, Afnan Abou Elwafa, and Heba Kurdi. "Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain." Applied Sciences 11, no. 2 (January 15, 2021): 796. http://dx.doi.org/10.3390/app11020796.

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Dataset size is considered a major concern in the medical domain, where lack of data is a common occurrence. This study aims to investigate the impact of dataset size on the overall performance of supervised classification models. We examined the performance of six widely-used models in the medical field, including support vector machine (SVM), neural networks (NN), C4.5 decision tree (DT), random forest (RF), adaboost (AB), and naïve Bayes (NB) on eighteen small medical UCI datasets. We further implemented three dataset size reduction scenarios on two large datasets and analyze the performance of the models when trained on each resulting dataset with respect to accuracy, precision, recall, f-score, specificity, and area under the ROC curve (AUC). Our results indicated that the overall performance of classifiers depend on how much a dataset represents the original distribution rather than its size. Moreover, we found that the most robust model for limited medical data is AB and NB, followed by SVM, and then RF and NN, while the least robust model is DT. Furthermore, an interesting observation is that a robust machine learning model to limited dataset does not necessary imply that it provides the best performance compared to other models.

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Dissertations / Theses on the topic "LIMITED DATASET"

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Chen, Dan. "An adaptive weighting algorithm for limited dataset verification problems." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B3204897X.

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Chen, Dan, and 陳丹. "An adaptive weighting algorithm for limited dataset verification problems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B3204897X.

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Patel, Rahul. "Maximum Likelihood – Expectation Maximum Reconstruction with Limited Dataset for Emission Tomography." Akron, OH : University of Akron, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=akron1175781554.

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Thesis (M.S.)--University of Akron, Dept. of Biomedical Engineering, 2007.
"May, 2007." Title from electronic thesis title page (viewed 04/26/2009) Advisor, Dale Mugler; Co-Advisor, Anthony Passalaqua; Committee member, Daniel Sheffer; Department Chair, Daniel Sheffer; Dean of the College, George K. Haritos; Dean of the Graduate School, George R. Newkome. Includes bibliographical references.

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Pinto, Cecilia. "Integration of multiple sparse and limited datasets helps to inform spatial conservation for an endangered marine species." Thesis, University of Aberdeen, 2015. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=228567.

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To develop spatial conservation strategies for endangered species it is necessary to understand the drivers of their population dynamics through time and space. When dealing with data limited species it is fundamentally important to develop and apply methods that identify which parameters future data collection should focus on to fill critical knowledge gaps which hinder robust decision making for spatial management. The aim of this study was to integrate data from independent sources in the parameterisation of a spatially realistic individual-based model to explore the potential of defining conservation measures for an endangered species. The case study species in this thesis is Dipturus sp. intermedia (flapper skate), a species that is the focus of conservation attention especially off the West Coast of Scotland. In order to estimate annual survival and transition probabilities between sampling sites, a multisite capture-mark-recapture model was developed in a Bayesian framework, accounting for the heterogeneous effort and individual heterogeneity in the data. Annual survival for the flapper skate was estimated to have been strongly decreasing in the last 30 years. The population showed high residency between sampling sites but connectivity is still present along the sea lochs of the West coast of Scotland. Vertical movement behaviour of flapper skate was found to be mainly driven by the cycle of spring and neap tides. An environmental suitability model suggested that flapper skate distribution is mainly driven by depth and distance from the coast. A novel approach was taken to validate the suitability predictions by using geolocated locations obtained from the vertical movement data. Finally the parameters estimated in the previous chapters were integrated in a last chapter applying an individual based spatial dynamic model to test its potential in defining conservation measures. To illustrate the potential of this approach for conservation planning, nine different scenarios were run testing different levels of fishing mortality and different spatial extent of fishing mortality. This work has demonstrated that, even when individual data sources are of moderate or low quality, combining multiple data types with analysis using contemporary statistical methods and the use of emerging spatial demographic models can provide a valuable approach to inform urgent conservation decisions.

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Louws, Margie. "Electronic Multi-agency Collaboration. A Model for Sharing Children¿s Personal Information Among Organisations." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/5694.

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The sharing of personal information among health and social service organisations is a complex issue and problematic process in present-day England. Organisations which provide services to children face enormous challenges on many fronts. Internal ways of working, evolving best practice, data protection applications, government mandates and new government agencies, rapid changes in technology, and increasing costs are but a few of the challenges with which organisations must contend in order to provide services to children while keeping in step with change. This thesis is an exploration into the process of sharing personal information in the context of public sector reforms. Because there is an increasing emphasis of multi-agency collaboration, this thesis examines the information sharing processes both within and among organisations, particularly those providing services to children. From the broad principles which comprise a socio-technical approach of information sharing, distinct critical factors for successful information sharing and best practices are identified. These critical success factors are then used to evaluate the emerging national database, ContactPoint, highlighting particular areas of concern. In addition, data protection and related issues in the information sharing process are addressed. It is argued that one of the main factors which would support effective information sharing is to add a timeline to the life of a dataset containing personal information, after which the shared information would dissolve. Therefore, this thesis introduces Dynamic Multi-Agency Collaboration (DMAC), a theoretical model of effective information sharing using a limited-life dataset. The limited life of the DMAC dataset gives more control to information providers, encouraging effective information sharing within the parameters of the Data Protection Act 1998.

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Louws, Margie. "Electronic multi-agency collaboration : a model for sharing children's personal information among organisations." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/5694.

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The sharing of personal information among health and social service organisations is a complex issue and problematic process in present-day England. Organisations which provide services to children face enormous challenges on many fronts. Internal ways of working, evolving best practice, data protection applications, government mandates and new government agencies, rapid changes in technology, and increasing costs are but a few of the challenges with which organisations must contend in order to provide services to children while keeping in step with change. This thesis is an exploration into the process of sharing personal information in the context of public sector reforms. Because there is an increasing emphasis of multi-agency collaboration, this thesis examines the information sharing processes both within and among organisations, particularly those providing services to children. From the broad principles which comprise a socio-technical approach of information sharing, distinct critical factors for successful information sharing and best practices are identified. These critical success factors are then used to evaluate the emerging national database, ContactPoint, highlighting particular areas of concern. In addition, data protection and related issues in the information sharing process are addressed. It is argued that one of the main factors which would support effective information sharing is to add a timeline to the life of a dataset containing personal information, after which the shared information would dissolve. Therefore, this thesis introduces Dynamic Multi-Agency Collaboration (DMAC), a theoretical model of effective information sharing using a limited-life dataset. The limited life of the DMAC dataset gives more control to information providers, encouraging effective information sharing within the parameters of the Data Protection Act 1998.

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Books on the topic "LIMITED DATASET"

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Ligon, Gina Scott, Douglas C. Derrick, and Mackenzie Harms. Destruction Through Collaboration. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190222093.003.0013.

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Terrorism provides a rich yet understudied domain to examine creative teams. Because teams working in a violent ideological organization must continue to generate covert and novel ways to recruit members, raise finances, and plan attacks, theories of creativity typically applied to more conventional organizations should also apply to terrorist teams. However, with limited empirical data about this phenomenon, it is unclear which tenets of creative research hold versus which do not translate in the domain of violent extremist organizations. The present effort reviews the extant literature on malevolent creativity, as well as examines predictors of innovation in a longitudinal dataset of teams operating in 50 terrorist organizations. In addition, a case study of a historically creative team operating in the Aum Shinrikyo is examined.

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Colli, Andrea, and Michelangelo Vasta. Italy. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198717973.003.0011.

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This chapter, by merging a qualitative and quantitative approach, focuses on the evolution of business groups in Italy during the twentieth century. By adopting network analysis, and by using a large and comprehensive dataset, the authors offer various proxy measures of the relevance of the largest business groups in the Italian economy. By also providing a taxonomy, the analysis clearly shows the persistence of large and entangled business groups in the Italian economy. Moreover, it shows that business groups are present not only among large firms, but in almost all the dimensional and juridical forms of Italian firms. The chapter, by challenging the conventional wisdom, confirms that business groups are neither limited to the less developed countries, nor are simply a second-best functional substitute of the M‐form characterizing big business around the world.

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Loporcaro, Michele. Romance gender systems. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199656547.003.0004.

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After showing that, for purposes of reconstruction, the dataset must be limited to non-creolized Romance varieties, the chapter discusses the notion ‘remnants of the neuter’, showing that this label covers disparate things, and that what is in focus here is morphosyntactically functional remnants, i.e. traces of a third (controller and/or target) gender. These are then inventoried, showing that almost all Romance languages preserve a third series of targets (in pronouns) for agreement with non-nominal controllers, and Sursilvan has this also on predicative adjectives. Furthermore, Romanian and many Italo-Romance dialects still have a third controller gender, and a subset of the latter even has an additional target gender, with dedicated agreement forms for either (in just one Calabrian dialect) the neuter plural or (in most dialects between the Roma–Ancona line and a line crossing central Puglia and northern Lucania) a neuter hosting just mass nouns (and hence, only singular).

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Kasprzak, Jaroslaw D., Anita Sadeghpour, and Ruxandra Jurcut. Doppler echocardiography. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198726012.003.0003.

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Doppler examination is an integral part of the echocardiogram. Current systems are equipped with spectral Doppler in continuous wave mode (offering measurements of high velocities with limited spatial specificity due to integration of signal along the scan line), pulsed wave mode (high spatial specificity with maximal recordable velocity reduced by the Nyquist limit), and colour Doppler flow mapping (allowing rapid identification of flow pattern within a cross-sectional B-mode sector). Tissue Doppler echocardiography emerged as a basic tool for sampling regional myocardial velocities, in pulsed wave or colour velocity mapping mode. Finally, three-dimensional systems improve spatial presentation of flow phenomena by integrating Doppler-derived flow patterns in three-dimensional datasets.

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Koinova, Maria. Diaspora Entrepreneurs and Contested States. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198848622.001.0001.

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Why do conflict-generated diasporas mobilize in contentious and non-contentious ways or use mixed strategies of contention? Why do they channel their homeland-oriented goals through host-states, transnational networks, and international organizations? This book develops a theory of socio-spatial positionality and its implications for the individual agency of diaspora entrepreneurs, moving beyond essentialized notions of diasporas as groups. Individual diaspora entrepreneurs operate in transnational social fields affecting their mobilizations beyond dynamics confined to host-states and original home-states. There are four types of diaspora entrepreneurs—Broker, Local, Distant, and Reserved—depending on the relative strength of their socio-spatial linkages to host-land, on the one hand, and original homeland and other global locations, on the other. A two-level typological theory captures nine causal pathways, unravelling how the socio-spatial linkages of these diaspora entrepreneurs interact with external factors: host-land foreign policies, homeland governments, parties, non-state actors, and critical events or limited global influences. Such pathways produce mobilization trajectories with varying levels of contention and methods of channelling homeland-oriented goals. Non-contentious pathways often occur when host-state foreign policies are convergent with the diaspora entrepreneurs’ goals, and when diaspora entrepreneurs can act autonomously. Dual-pronged contention pathways occur quite often, under the influence of homeland governments, non-state actors, and political parties. The most contentious pathway occurs in response to violent critical events in the homeland or adjacent to it fragile states. This book is informed by 300 interviews and a dataset of 146 interviews with diaspora entrepreneurs among the Albanian, Armenian, and Palestinian diasporas in the UK, Germany, France, Netherlands, Sweden, and Switzerland, as well as Kosovo and Armenia in the European neighbourhood.

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Delsol, Laurent. Nonparametric Methods for α-Mixing Functional Random Variables. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.5.

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This article considers how functional kernel methods can be used to study α-mixing datasets. It first provides an overview of how prediction problems involving dependent functional datasets may arise from the study of time series, focusing on the standard discretized model and modelization that takes into account the functional nature of the evolution of the quantity to be studied over time. It then considers strong mixing conditions, with emphasis on the notion of α-mixing coefficients and α-mixing variables introduced by Rosenblatt (1956). It also describes some conditions for a Markov chain to be α-mixing; some useful tools that provide covariance inequalities, exponential inequalities, and Central Limit Theorem (CLT) for α-mixing sequences; the asymptotic properties of functional kernel estimators; the use of kernel smoothing methods with α-mixing datasets; and various functional kernel estimators corresponding to different prediction methods. Finally, the article highlights some interesting prospects for further research.

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Loyle, Cyanne E. Transitional Justice During Armed Conflict. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228637.013.218.

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Armed conflict is ultimately about the violent confrontation between two or more groups; however, there is a range of behaviors, both violent and nonviolent, pursued by governments and rebel groups while conflict is ongoing that impacts the course and outcomes of that violence. The use of judicial or quasi-judicial institutions during armed conflict is one such behavior. While there is a well-developed body of literature that examines the conditions under which governments engage with the legacies of violence following armed conflict, we know comparatively little about these same institutions used while conflict is ongoing.Similar to the use of transitional justice following armed conflict or post-conflict justice, during-conflict transitional justice (DCJ) refers to “a judicial or quasi-judicial process initiated during an armed conflict that attempts to address wrongdoings that have taken or are taking place as part of that conflict” (according to Loyle and Binningsbø). DCJ includes a variety of institutional forms pursued by both governments and rebel groups such as human rights trials, truth commissions or commissions of inquiry, amnesty offers, reparations, purges, or exiles.As our current understanding of transitional justice has focused exclusively on these processes following a political transition or the termination of an armed conflict, we have a limited understanding of how and why these processes are used during conflict. Extant work has assumed, either implicitly or explicitly, that transitional justice is offered and put in place once violence has ended, but this is not the case. New data on this topic from the During-Conflict Justice dataset by Loyle and Binningsbø suggests that the use of transitional justice during conflict is a widespread and systematic policy across multiple actor groups. In 2017, Loyle and Binningsbø found that DCJ processes were used during over 60% of armed conflicts from 1946 through 2011; and of these processes 10% were put in place by rebel groups (i.e., the group challenging the government rather than the government in power).Three main questions arise from this new finding: Under what conditions are justice processes implemented during conflict, why are these processes put in place, and what is the likely effect of their implementation on the conflict itself? Answering these questions has important implications for understanding patterns of government and rebel behavior while conflict is ongoing and the impacts of those behaviors. Furthermore, this work helps us to broaden our understanding of the use of judicial and quasi-judicial processes to those periods where no power shift has taken place.

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Book chapters on the topic "LIMITED DATASET"

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Xing, Yujie, Itishree Mohallick, Jon Atle Gulla, Özlem Özgöbek, and Lemei Zhang. "An Educational News Dataset for Recommender Systems." In ECML PKDD 2020 Workshops, 562–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65965-3_39.

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AbstractDatasets are an integral part of contemporary research on recommender systems. However, few datasets are available for conventional recommender systems and even very limited datasets are available when it comes to contextualized (time and location-dependent) News Recommender Systems. In this paper, we introduce an educational news dataset for recommender systems. This dataset is the refined version of the earlier published Adressa dataset and intends to support the university students in the educational purpose. We discuss the structure and purpose of the refined dataset in this paper.

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Sahu, Priyanka, Anuradha Chug, Amit Prakash Singh, and Dinesh Singh. "Enhancing Leaf Disease Identification with GAN for a Limited Training Dataset." In Image Processing and Intelligent Computing Systems, 195–206. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003267782-13.

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Bergui, Mohammed, Nikola S. Nikolov, and Said Najah. "Hadoop Dataset for Job Estimation in the Cloud with Limited Bandwidth." In Lecture Notes in Networks and Systems, 341–48. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-28073-3_24.

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García-Álvarez, David, and Javier Lara Hinojosa. "Global Thematic Land Use Cover Datasets Characterizing Agricultural Covers." In Land Use Cover Datasets and Validation Tools, 399–417. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_20.

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AbstractThere is a wide variety of global thematic Land Use Cover (LUC) datasets characterizing agricultural covers. Most of them focus on cropland areas, providing information on their extent or the percentage of cropland cover on the ground. In some cases, the focus is more specific and they provide information on cropland irrigation practices. In other cases, specific maps charting the extension of different crops are also available. In this chapter, we review 8 different datasets with a spatial resolution of at least 1 km. There are many other datasets characterizing agricultural covers at coarser resolutions, such as the Historic Croplands Dataset, GMRCA or GIAM. Their coarse resolution hampers their potential application in practice, which is why they are not described in detail in this chapter. Nor do we analyse FROM-GC, a dataset mapping the extent of global cropland at 30 m, because it is not currently accessible. GFSAD30 has the highest resolution of all the datasets reviewed (30 m). It also provides some of the most up-to-date information (2015). However, it only charts the extent of cropland. As part of an associated project, GFSAD1KCD and GFSAD1KCM characterize cropland areas in 9 and 7 categories respectively at 1 km for 2010. They provide information on the irrigation status of the crops. GFSAD1KCD and GFSAD1KCM were obtained from data fusion. This method is commonly used in the production of many of the cropland datasets reviewed: IIASA-IFPRI cropland map, Global Synergy Cropland Map, Unified Cropland Layer (UCL) and ASAP Land Cover Masks. The IIASA-IFPRI (2005) and ASAP maps provide information on the proportion of cropland at a spatial resolution of 1 km. ASAP also includes a map on rangeland covers, and as such is the only dataset described in this chapter that maps a cover other than croplands. The Global Synergy Cropland Map (2010) and the Unified Cropland Layer (2014) also map cropland proportions, although they have been produced at higher spatial resolutions: 500 and 250 m respectively. The Global Cropland Extent product maps the extent of cropland at 250 m based on imagery from 2000-2008. Although thematically limited, this dataset is less affected by time variability, as it is based on imagery taken over a long period (8 years). Finally, GRIPC maps the extent of three types of cropland area (irrigated, rainfed and paddy crops) at 500 m for 2005.

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Milicevic, Mario, Krunoslav Zubrinic, Ines Obradovic, and Tomo Sjekavica. "Application of Transfer Learning for Fine-Grained Vessel Classification Using a Limited Dataset." In Lecture Notes in Electrical Engineering, 125–31. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21507-1_19.

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Daniels, Reza Che. "A Framework for Investigating Microdata Quality, with Application to South African Labour Market Household Surveys." In How Data Quality Affects our Understanding of the Earnings Distribution, 7–36. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3639-5_2.

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AbstractThis chapter identifies a framework for investigating microdata quality that is particularly useful to researchers working with public-use micro datasets where limited information about the data quality protocols of the survey organisation are present. It then utilises this framework to investigate South African labour market household surveys from the mid 1990s to 2007. In order to develop the framework, we rely on the total survey error (TSE) framework to articulate the forms of statistical imprecision that exist in any public-use dataset. The magnitudes of statistical imprecision are largely dependent on the efficacy of the survey organisation’s data quality control protocols, which are, in turn, affected by human resource and budget constraints.

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Ciavotta, Michele, Vincenzo Cutrona, Flavio De Paoli, Nikolay Nikolov, Matteo Palmonari, and Dumitru Roman. "Supporting Semantic Data Enrichment at Scale." In Technologies and Applications for Big Data Value, 19–39. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-78307-5_2.

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AbstractData enrichment is a critical task in the data preparation process in which a dataset is extended with additional information from various sources to perform analyses or add meaningful context. Facilitating the enrichment process design for data workers and supporting its execution on large datasets are only supported to a limited extent by existing solutions. Harnessing semantics at scale can be a crucial factor in effectively addressing this challenge. This chapter presents a comprehensive approach covering both design- and run-time aspects of tabular data enrichment and discusses our experience in making this process scalable. We illustrate how data enrichment steps of a Big Data pipeline can be implemented via tabular transformations exploiting semantic table annotation methods and discuss techniques devised to support the enactment of the resulting process on large tabular datasets. Furthermore, we present results from experimental evaluations in which we tested the scalability and run-time efficiency of the proposed cloud-based approach, enriching massive datasets with promising performance.

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Costa, Arthur C., Helder C. R. Oliveira, Juliana H. Catani, Nestor de Barros, Carlos F. E. Melo, and Marcelo A. C. Vieira. "Detection of Architectural Distortion with Deep Convolutional Neural Network and Data Augmentation of Limited Dataset." In XXVI Brazilian Congress on Biomedical Engineering, 155–59. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-2517-5_24.

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Sharma, Pranav, Marcus Rüb, Daniel Gaida, Heiko Lutz, and Axel Sikora. "Deep Learning in Resource and Data Constrained Edge Computing Systems." In Machine Learning for Cyber Physical Systems, 43–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_5.

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AbstractTo demonstrate how deep learning can be applied to industrial applications with limited training data, deep learning methodologies are used in three different applications. In this paper, we perform unsupervised deep learning utilizing variational autoencoders and demonstrate that federated learning is a communication efficient concept for machine learning that protects data privacy. As an example, variational autoencoders are utilized to cluster and visualize data from a microelectromechanical systems foundry. Federated learning is used in a predictive maintenance scenario using the C-MAPSS dataset.

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Giorgini, Eugenia, Enrica Vecchi, Luca Poluzzi, Luca Tavasci, Maurizio Barbarella, and Stefano Gandolfi. "15 Years of the Italian GNSS Geodetic Reference Frame (RDN): Preliminary Analysis and Considerations." In Geomatics for Green and Digital Transition, 3–14. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17439-1_1.

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AbstractIn 2011, the IGMI (Istituto Geografico Militare Italiano) defined the new Italian geodetic reference, materialized by the Rete Dinamica Nazionale (RDN), a cluster of 99 GNSS permanent stations located in Italy and, few of them, in neighbouring areas. RDN also includes some IGS and EPN sites, so that it constitutes a densification of those two networks. The official coordinates of the 99 GNSS stations were initially obtained by computing a limited period of 28 days starting from the end of 2007 and aligned to the datum ETRS89-ETRF2000 at epoch 2008.0. After years of continuously acquired data, other studies published the stations’ coordinates together with the associated velocities. This paper presents the updated results of the velocity trends considering the whole dataset now available, consisting of 15 years of data. The analysis considered only the 77 stations that worked consistently for at least five years. The workflow starts with the archive organization and pre-analysis, followed by the geodetic computation using the Precise Point Positioning approach implemented in the GIPSYX software. After the post-processing of the solutions, which included the alignment to the ETRF2000 frame and the analysis of discontinuities, the mean velocities have been computed. The latter were compared to those estimated in a previous work basing on 8 years long dataset. The comparison shows the overall agreement between the linear trends, but also highlights the importance of considering the whole dataset nowadays available to assess the behaviour of those few sites who underwent velocity changes over time.

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Conference papers on the topic "LIMITED DATASET"

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De, Vidisha, T. T. Teo, W. L. Woo, and T. Logenthiran. "Photovoltaic Power Forecasting using LSTM on Limited Dataset." In 2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). IEEE, 2018. http://dx.doi.org/10.1109/isgt-asia.2018.8467934.

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Desai, Shrinivas D., Shantala Giraddi, Nitin Verma, Puneet Gupta, and Sharan Ramya. "Breast Cancer Detection Using GAN for Limited Labeled Dataset." In 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2020. http://dx.doi.org/10.1109/cicn49253.2020.9242551.

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Jess, Torben, Philip Woodall, and Duncan McFarlane. "Overcoming limited dataset availability when working with industrial organisations." In 2015 IEEE 13th International Conference on Industrial Informatics (INDIN). IEEE, 2015. http://dx.doi.org/10.1109/indin.2015.7281843.

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Jonnalagedda, Padmaja, Brent Weinberg, Jason Allen, Taejin L. Min, Shiv Bhanu, and Bir Bhanu. "SAGE: Sequential Attribute Generator for Analyzing Glioblastomas Using Limited Dataset." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412151.

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Komasilovs, Vitalijs, Aleksejs Zacepins, Armands Kviesis, and Claudio Estevez. "Traffic Monitoring using an Object Detection Framework with Limited Dataset." In 5th International Conference on Vehicle Technology and Intelligent Transport Systems. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007586800002179.

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Komasilovs, Vitalijs, Aleksejs Zacepins, Armands Kviesis, and Claudio Estevez. "Traffic Monitoring using an Object Detection Framework with Limited Dataset." In 5th International Conference on Vehicle Technology and Intelligent Transport Systems. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007586802910296.

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Adke, Gaurav. "Application of GAN for Reducing Data Imbalance under Limited Dataset." In 17th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010782800003124.

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Li, Enke, and Lingrui Mei. "Traffic Sign Recognition and Retrieval Using Limited Dataset in the Wild." In 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2021. http://dx.doi.org/10.1109/imcec51613.2021.9482122.

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Prakoso, Hamdan, Ridi Ferdiana, and Rudy Hartanto. "Indonesian Automatic Speech Recognition system using CMUSphinx toolkit and limited dataset." In 2016 International Symposium on Electronics and Smart Devices (ISESD). IEEE, 2016. http://dx.doi.org/10.1109/isesd.2016.7886734.

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Lebbad, Anderson, Garrett Clayton, and C. Nataraj. "Classification of UXO Using Convolutional Networks Trained on a Limited Dataset." In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2017. http://dx.doi.org/10.1109/icmla.2017.000-1.

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Reports on the topic "LIMITED DATASET"

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Garton, Timothy. Data enrichment and enhanced accessibility of waterborne commerce numerical data : spatially depicting the National Waterway Network. Engineer Research and Development Center (U.S.), December 2020. http://dx.doi.org/10.21079/11681/39223.

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This report provides methodologies and processes of data enrichment and enhanced accessibility of Waterborne Commerce and Statistics Center (WCSC) maintained databases. These databases house tabular and statistical data that reports on The U.S. Army Corps of Engineers (USACE) Civil Works Division National Waterway Network (NWN), which geospatially represents approximately 1,000 harbors and 25,000 miles of channels and waterways. WCSC is a division of The Institute for Water Resources (IWR). They have been tasked with the international collection, maintenance, and archival of all records involving commercial movements and commerce that occur on federal waterways. The current records structure is a large, tabular dataset and limited to the systems and processes put in place prior to the computing standards and capabilities available today. Methods have been tested and utilized to bring the tabular datasets into an optimized, modern geospatial network and expanded upon to create a higher resolution than previously maintained by the WCSC. This report will expand upon the applied methodologies to optimize data queries and the overall enhancement of the data system to allow for linkages to various other sources of information for commerce data enhancement for decision support assistance.

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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li, and Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.

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This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavement distress, particularly as seen by patch settling. This method was far more promising. In addition, algorithms were developed for segmentation of the GPR data and for classification of the ambient pavement and the locations and types of patches found in it. The results so far are promising but far from perfect, with a relatively high rate of false alarms. The two project parts were combined to produce a fused patching table. Several hundred miles of data was captured with the Waylink System to compare with a much more limited GPR dataset. The primary dataset was captured on I-74. A software application for MATLAB has been written to aid in automation of patch table creation.

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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li, and Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.

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This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavement distress, particularly as seen by patch settling. This method was far more promising. In addition, algorithms were developed for segmentation of the GPR data and for classification of the ambient pavement and the locations and types of patches found in it. The results so far are promising but far from perfect, with a relatively high rate of false alarms. The two project parts were combined to produce a fused patching table. Several hundred miles of data was captured with the Waylink System to compare with a much more limited GPR dataset. The primary dataset was captured on I-74. A software application for MATLAB has been written to aid in automation of patch table creation.

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Koduru, Smitha, and Jason Skow. PR-244-153719-R01 Quantification of ILI Sizing Uncertainties and Improving Correction Factors. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 2018. http://dx.doi.org/10.55274/r0011518.

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Operators routinely perform verification digs to assess whether an inline inspection (ILI) tool meets the performance specified by the ILI vendors. Characterizing the actual ILI tool performance using available field and ILI data is a difficult problem due to uncertainties associated with measurements and geometric classification of features. The focus of this project is to use existing ILI and excavation data to develop better approaches for assessing ILI tool performance. For corrosion features, operators are primarily interested in quantifying magnetic flux leakage (MFL) ILI tool sizing error and its relationship to burst pressure estimates. In previously completed PRCI research, a limited MFL ILI dataset was used to determine the corrosion feature depth sizing bias and random error using principles published in API 1163 (2013). The research demonstrated the tendency for ILI predictions to be slightly lower than field measurements (i.e., under-call) for the dataset studied, and it provided a framework for characterizing this bias. The goal of this project was to expand on previous work by increasing the number and type of feature morphologies available for analysis, and by estimating the sizing error of ILI measured external corrosion features. New geometric classification criteria, complementing the current criteria suggested by the Pipeline Operator Forum (POF 2009), were also investigated. Lastly, correction factors based on burst pressure prediction accuracy were developed to account for the effect of adopting various feature interaction rules. This report has a related webinar (member login required).

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Gregor, Nicholas, Kofi Addo, Linda Al Atik, Gail Atkinson, David Boore, Yousef Bozorgnia, Kenneth Campbell, et al. Comparison of NGA-Sub Ground-Motion Models. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/ubdv7944.

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Ground-motion models (GMMs) for subduction earthquakes recently developed as part of the NGA-Subduction (NGA-Sub) project are compared in this report. The three models presented in this comparison report are documented in their respective PEER reports. Two of the models are developed for a global version and as well regionalized models. The third model is developed based on earthquakes contain in the NGA-Sub dataset only from Japan and as such is applicable for Japan. As part of the comparisons presented in this report, deterministic calculations are provided for the global and regional cases amongst the models. The digital values and additional plots from these deterministic comparisons are provided as part of the electronic supplement for this report. In addition, ground-motion estimates are provided for currently published subduction GMMs. Two example probabilistic seismic hazard analysis calculations are also presented for two sites located in the Pacific Northwest Region in the state of Washington. Based on the limited comparisons presented in this report, a general understanding of these new GMMs can be appreciated with the expectation that the implementation for a specific seismic hazard study should incorporate similar and additional comparisons and sensitivity studies similar to the ones presented in this report.

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Zeballos, Eliana, and Wilson Sinclair. Estimating the state-level food expenditure series. Washington, D.C.: USDA Economic Research Service, April 2023. http://dx.doi.org/10.32747/2023.8023696.ers.

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The USDA, Economic Research Service's (ERS) Food Expenditure Series (FES) is a comprehensive measure of the total value of food acquired in the United States over time. FES provides users with data to evaluate changes in food spending and its composition; however, FES is limited to the national level. This report presents the methodology and data used to generate food expenditure estimates at the State level. The State-level FES follows a similar methodology used in the national level but with a different underlying dataset and benchmarked to the national-level estimates. The national-level estimates are based primarily on food sales reported in the U.S. Department of Commerce, Bureau of the Census' Economic Census, which is published every 5 years, and uses three annual surveys to interpolate between years and extrapolate lagged data forward. The State-level FES estimates are based primarily on sales reported in the National Establishment Time Series Database. The database provides time-series data at the establishment level across all sectors, including grocery stores and food service outlets. The State-level FES can be used by government agencies, academics, the public, and other stakeholders to understand differences in consumer food acquisitions and spending behavior at a more granular level.

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Mackie, S. J., C. M. Furlong, P. K. Pedersen, and O. H. Ardakani. Stratigraphy, facies heterogeneities, and structure in the Montney Formation of northeastern British Columbia: relation to H2S distribution. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329796.

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In the last decade, the Lower Triassic Montney Formation of the Western Canada Sedimentary Basin (WCSB) has undergone significant development, providing a rich dataset to study structural, stratigraphic, and facies control on the variations in hydrogen sulphide (H2S) gas content. Splitting the siltstone dominated Montney into the three regional members (i.e., Lower Montney, Middle Montney, and Upper Montney) allows for detailed analysis on the enrichment of H2S within a local-scale study area in northeastern British Columbia (BC). Within this study area, Upper Montney H2S content increases within individual parasequences both up-dip and towards the east. In addition to potential up-dip migration, there may be greater sulphur-bearing components in the east, allowing for the sulphate reduction required to generate H2S. The overlying Middle Triassic thins eastward, providing proximity to the overlying anhydrite-rich beds of the Upper Triassic Charlie Lake Formation. Further, the overlying Middle Triassic Sunset Prairie Formation has an erosional edge that corresponds with eastern elevated H2S concentrations within the Upper Montney unit. Mapped structures are syn-depositional to the Middle Triassic, potentially providing conduits for early sulphate-rich fluid migration. In the Middle and Lower Montney, elevated H2S generally occurs with proximity to the top of the Permian Belloy Formation. Within this study area, limited Lower Montney data is available and thus needs to be further corroborated with regional data. Both the Middle and Lower Montney display elevated H2S in trends that generally align with mapped faults. The faults may have acted as conduits for sulphate-rich fluids to migrate during early burial then migrate laterally through facies that may have been permeable during early burial, such as the carbonate-rich facies at the boundary between the Middle and Lower Montney. Further core and isotope analyses are required to fully understand this relationship.

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Panek, Jeffrey, Adrian Huth, Alan Krol, and James McCarthy. PR-312-18208-R03 AERMOD Performance Assessments, Implementation Issues and Recommended Improvements. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 2022. http://dx.doi.org/10.55274/r0012232.

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In 2010, the U.S. Environmental Protection Agency (EPA) adopted a 1-hour nitrogen dioxide (NO2) National Ambient Air Quality Standard (NAAQS) of 100 parts per billion (ppb) or approximately 188 micrograms per cubic meter (�g/m3) that is considerably more stringent than the longstanding annual standard of 53 ppb (100 �g/m3). New or modified compressor units may be encumbered by federal or state regulatory requirements to demonstrate compliance with the NO2 NAAQS using AERMOD, EPA's dispersion model, because the new NAAQS greatly reduces the compliance margin. Compressor stations have been increasingly requested to model source contribution to other nearby permitting actions through no new action on their part. Model conservatism and performance concerns has limited NO2 NAAQS compliance options necessitating the need to improve model estimates for reciprocating engine drivers at pipeline compressor stations. AERMOD was developed and validated with a primary focus on larger sources with taller stacks, such as electric utility boilers, which results in model conservatism for sources such as compressor stations with shorter stacks that result in near-field modeled impacts. This report summarizes additional analyses conducted and reviewed with EPA that were completed to assess and reduce model conservatism and improve overall model performance. This report presents a more detailed analysis of modeled versus observed results, model performance, and recommendations for model improvements. These analyses also evaluated other ongoing efforts (e.g., PRIME2 downwash improvements and integration of the ADMS chemistry module an alternative in AERMOD) using the data collected from this program may be used to assess these revisions. This final report summarizes the deeper dive into the NOx chemistry, dispersion, and downwash performance assessments within AERMOD based on the Balko dataset. Specific recommendations are made throughout this report to improve overall model performance.

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McMartin, I., M. S. Gauthier, and A. V. Page. Updated post-glacial marine limits along western Hudson Bay, central mainland Nunavut and northern Manitoba. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330940.

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A digital compilation of updated postglacial marine limits was completed in the coastal regions of central mainland Nunavut and northern Manitoba between Churchill and Queen Maud Gulf. The compilation builds on and updates previous mapping of the marine limits at an unprecedented scale, making use of high-resolution digital elevation models, new field-based observations of the marine limit and digital compilations of supporting datasets (i.e. marine deltas and marine sediments). The updated mapping also permits a first-hand, knowledgedriven interpolation of a continuous limit of marine inundation linking the Tyrrell Sea to Arctic Ocean seawaters. The publication includes a detailed description of the mapping methods, a preliminary interpretation of the results, and a GIS scalable layout map for easy access to the various layers. These datasets and outputs provide robust constraints to reconstruct the patterns of ice retreat and for glacio-isostatic rebound models, important for the estimation of relative sea level changes and impacts on the construction of nearshore sea-transport infrastructures. They can also be used to evaluate the maximum extent of marine sediments and associated permafrost conditions that can affect land-based infrastructures, and potential secondary processes related to marine action in the surficial environment and, therefore, can enhance the interpretation of geochemical anomalies in glacial drift exploration methods. A generalized map of the maximum limit of postglacial marine inundation produced for map representation and readability also constitutes an accessible output relevant to Northerners and other users of geoscience data.

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Hilbrecht, Margo, David Baxter, Alexander V. Graham, and Maha Sohail. Research Expertise and the Framework of Harms: Social Network Analysis, Phase One. GREO, December 2020. http://dx.doi.org/10.33684/2020.006.

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In 2019, the Gambling Commission announced a National Strategy to Reduce Gambling Harms. Underlying the strategy is the Framework of Harms, outlined in Measuring gambling-related harms: A framework for action. "The Framework" adopts a public health approach to address gambling-related harm in Great Britain across multiple levels of measurement. It comprises three primary factors and nine related subfactors. To advance the National Strategy, all componentsneed to be supported by a strong evidence base. This report examines existing research expertise relevant to the Framework amongacademics based in the UK. The aim is to understand the extent to which the Framework factors and subfactors have been studied in order to identify gaps in expertise and provide evidence for decision making thatisrelevant to gambling harms research priorities. A social network analysis identified coauthor networks and alignment of research output with the Framework. The search strategy was limited to peer-reviewed items and covered the 12-year period from 2008 to 2019. Articles were selected using a Web of Science search. Of the 1417 records identified in the search, the dataset was refined to include only those articles that could be assigned to at least one Framework factor (n = 279). The primary factors and subfactors are: Resources:Work and Employment, Money and Debt, Crime;Relationships:Partners, Families and Friends, Community; and Health:Physical Health, Psychological Distress, and Mental Health. We used Gephi software to create visualisations reflecting degree centrality (number of coauthor networks) so that each factor and subfactor could be assessed for the density of research expertise and patterns of collaboration among coauthors. The findings show considerable variation by framework factor in the number of authors and collaborations, suggesting a need to develop additional research capacity to address under-researched areas. The Health factor subcategory of Mental Health comprised almost three-quarters of all citations, with the Resources factor subcategory of Money and Debt a distant second at 12% of all articles. The Relationships factor, comprised of two subfactors, accounted for less than 10%of total articles. Network density varied too. Although there were few collaborative networks in subfactors such as Community or Work and Employment, all Health subfactors showed strong levels of collaboration. Further, some subfactors with a limited number of researchers such as Partners, Families, and Friends and Money and debt had several active collaborations. Some researchers’ had publications that spanned multiple Framework factors. These multiple-factor researchers usually had a wide range of coauthors when compared to those who specialised (with the exception of Mental Health).Others’ collaborations spanned subfactors within a factor area. This was especially notable forHealth. The visualisations suggest that gambling harms research expertise in the UK has considerable room to grow in order to supporta more comprehensive, locally contextualised evidence base for the Framework. To do so, priority harms and funding opportunities will need further consideration. This will require multi-sector and multidisciplinary collaboration consistent with the public health approach underlying the Framework. Future research related to the present analysis will explore the geographic distribution of research activity within the UK, and research collaborations with harms experts internationally.

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