Relevant bibliographies by topics / Wine Review Dataset

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Academic literature on the topic 'Wine Review Dataset'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Wine Review Dataset"

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Tian, Qiuyun, Brittany Whiting, and Bernard Chen. "Wineinformatics: Comparing and Combining SVM Models Built by Wine Reviews from Robert Parker and Wine Spectator for 95 + Point Wine Prediction." Fermentation 8, no. 4 (2022): 164. http://dx.doi.org/10.3390/fermentation8040164.

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Wineinformatics is among the new fields in data science that use wine as domain knowledge. To process large amounts of wine review data in human language format, the computational wine wheel is applied. In previous research, the computational wine wheel was created and applied to different datasets of wine reviews developed by Wine Spectator. The goal of this research is to explore the development and application of the computational wine wheel to reviews from a different reviewer, Robert Parker. For comparison, this research collects 513 elite Bordeaux wines that were reviewed by both Robert
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Oliveira, Luís, Rodrigo Rocha Silva, and Jorge Bernardino. "Wine Ontology Influence in a Recommendation System." Big Data and Cognitive Computing 5, no. 2 (2021): 16. http://dx.doi.org/10.3390/bdcc5020016.

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Wine is the second most popular alcoholic drink in the world behind beer. With the rise of e-commerce, recommendation systems have become a very important factor in the success of business. Recommendation systems analyze metadata to predict if, for example, a user will recommend a product. The metadata consist mostly of former reviews or web traffic from the same user. For this reason, we investigate what would happen if the information analyzed by a recommendation system was insufficient. In this paper, we explore the effects of a new wine ontology in a recommendation system. We created our o
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Brandon, Gowray, Yadav Omprakash, Keerimolel Albert, Galsulkar Sharifa, and Revulagadda Akshita. "WordCloud Generation using NLP." Advancement of Computer Technology and its Applications 4, no. 2 (2021): 1–3. https://doi.org/10.5281/zenodo.4991876.

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Natural Language Processing is one of the most powerful tool that we have today for machines to understand how and what we are thinking and saying, what we mean with the power to understand sentence in various languages and process it to give us desired outputs in various instances like frequency of a particular word, summarization etc. WordCloud is one such application where we can using concepts of NLP to create beautiful and aesthetically pleasing, decorative wordclouds which can be used for various purposes such as on websites, greeting cards etc.
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Kopsacheilis, Orestis, Pantelis P. Analytis, Karthikeya Kaushik, Stefan M. Herzog, Bahador Bahrami, and Ophelia Deroy. "Crowdsourcing the assessment of wine quality: Vivino ratings, professional critics, and the weather." Journal of Wine Economics 19, no. 3 (2024): 285–304. https://doi.org/10.1017/jwe.2024.20.

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AbstractCrowdsourcing platforms—such as Vivino—that aggregate the opinions of large numbers of amateur wine reviewers represent a new source of information on the wine market. We assess the validity of aggregated Vivino ratings based on two criteria: correlation with professional critics’ ratings and sensitivity to weather conditions affecting the quality of grapes. We construct a large, novel dataset consisting of Vivino ratings for a portfolio of red wines from Bordeaux, review scores from professional critics, and weather data from a local weather station. Vivino ratings correlate substanti
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Puga, German, James Fogarty, Atakelty Hailu, and Alejandro Gennari. "Modeling Grape Price Dynamics in Mendoza: Lessons for Policymakers." Journal of Wine Economics 14, no. 4 (2019): 343–55. http://dx.doi.org/10.1017/jwe.2019.29.

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AbstractMendoza is the main wine-producing province of Argentina, and the government is currently implementing a range of policies that seek to improve grape grower profitability, including a vineyard replanting program. This study uses a dataset of all grape sales recorded in Mendoza from 2007 to 2018, totaling 90,910 observations, to investigate the determinants of grape prices. Key findings include: smaller volume transactions receive lower-average prices per kilogram sold; the discount for cash payments is higher in less-profitable regions; and the effect of wine stock levels on prices is
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Dong, Zeqing, Xiaowan Guo, Syamala Rajana, and Bernard Chen. "Understanding 21st Century Bordeaux Wines from Wine Reviews Using Naïve Bayes Classifier." Beverages 6, no. 1 (2020): 5. http://dx.doi.org/10.3390/beverages6010005.

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Wine has been popular with the public for centuries; in the market, there are a variety of wines to choose from. Among all, Bordeaux, France, is considered as the most famous wine region in the world. In this paper, we try to understand Bordeaux wines made in the 21st century through Wineinformatics study. We developed and studied two datasets: the first dataset is all the Bordeaux wine from 2000 to 2016; and the second one is all wines listed in a famous collection of Bordeaux wines, 1855 Bordeaux Wine Official Classification, from 2000 to 2016. A total of 14,349 wine reviews are collected in
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Gatou, Paraskevi, Xanthi Tsiara, Alexandros Spitalas, Spyros Sioutas, and Gerasimos Vonitsanos. "Artificial Intelligence Techniques in Grapevine Research: A Comparative Study with an Extensive Review of Datasets, Diseases, and Techniques Evaluation." Sensors 24, no. 19 (2024): 6211. http://dx.doi.org/10.3390/s24196211.

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In the last few years, the agricultural field has undergone a digital transformation, incorporating artificial intelligence systems to make good employment of the growing volume of data from various sources and derive value from it. Within artificial intelligence, Machine Learning is a powerful tool for confronting the numerous challenges of developing knowledge-based farming systems. This study aims to comprehensively review the current scientific literature from 2017 to 2023, emphasizing Machine Learning in agriculture, especially viticulture, to detect and predict grape infections. Most of
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Le, Long, Pedro Navarrete Hurtado, Ian Lawrence, Qiuyun Tian, and Bernard Chen. "Applying Neural Networks in Wineinformatics with the New Computational Wine Wheel." Fermentation 9, no. 7 (2023): 629. http://dx.doi.org/10.3390/fermentation9070629.

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Wineinformatics involves the application of data science techniques to wine-related datasets generated during the grape growing, wine production, and wine evaluation processes. Its aim is to extract valuable insights that can benefit wine producers, distributors, and consumers. This study highlights the potential of neural networks as the most effective black-box classification algorithm in wineinformatics for analyzing wine reviews processed by the Computational Wine Wheel (CWW). Additionally, the paper provides a detailed overview of the enhancements made to the CWW and presents a thorough c
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Chen, Bernard, Valentin Velchev, James Palmer, and Travis Atkison. "Wineinformatics: A Quantitative Analysis of Wine Reviewers." Fermentation 4, no. 4 (2018): 82. http://dx.doi.org/10.3390/fermentation4040082.

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Data Science is a successful study that incorporates varying techniques and theories from distinct fields including Mathematics, Computer Science, Economics, Business and domain knowledge. Among all components in data science, domain knowledge is the key to create high quality data products by data scientists. Wineinformatics is a new data science application that uses wine as the domain knowledge and incorporates data science and wine related datasets, including physicochemical laboratory data and wine reviews. This paper produces a brand-new dataset that contains more than 100,000 wine revie
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Dong, Zeqing, Travis Atkison, and Bernard Chen. "Wineinformatics: Using the Full Power of the Computational Wine Wheel to Understand 21st Century Bordeaux Wines from the Reviews." Beverages 7, no. 1 (2021): 3. http://dx.doi.org/10.3390/beverages7010003.

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Although wine has been produced for several thousands of years, the ancient beverage has remained popular and even more affordable in modern times. Among all wine making regions, Bordeaux, France is probably one of the most prestigious wine areas in history. Since hundreds of wines are produced from Bordeaux each year, humans are not likely to be able to examine all wines across multiple vintages to define the characteristics of outstanding 21st century Bordeaux wines. Wineinformatics is a newly proposed data science research with an application domain in wine to process a large amount of wine
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Books on the topic "Wine Review Dataset"

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Peacock, Janet L., Sally M. Kerry, and Raymond R. Balise. Presenting Medical Statistics from Proposal to Publication. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198779100.001.0001.

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Presenting Medical Statistics from Proposal to Publication (second edition) aims to show readers how to conduct a wide range of statistical analyses from sample size calculations through to multifactorial regressions that are needed in the research process. The second edition of ‘Presenting’ has been revised and updated and now includes Stata, SAS, SPSS, and R. The book shows how to interpret each computer output and illustrates how to present the results and accompanying text in a format suitable for a peer-reviewed journal article or research report. All analyses are illustrated using real d
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Lutz, Wolfgang, William P. Butz, and Samir KC, eds. World Population & Human Capital in the Twenty-First Century. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198813422.001.0001.

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Condensed into a detailed analysis and a selection of continent-wide datasets, this revised edition of World Population & Human Capital in the Twenty-First Century addresses the role of educational attainment in global population trends and models. Presenting the full chapter text of the original edition alongside a concise selection of data, it summarizes past trends in fertility, mortality, migration, and education, and examines relevant theories to identify key determining factors. Deriving from a global survey of hundreds of experts and five expert meetings on as many continents, World
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Book chapters on the topic "Wine Review Dataset"

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Choudhary, Gautam, Natwar Modani, and Nitish Maurya. "ReAct: A Review Comment Dataset for Actionability (and more)." In Web Information Systems Engineering – WISE 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91560-5_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. 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
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García-Álvarez, David, and Javier Lara Hinojosa. "Global Thematic Land Use Cover Datasets Characterizing Vegetation Covers." In Land Use Cover Datasets and Validation Tools. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_19.

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AbstractVegetation covers were one of the first land covers to receive special attention when thematic Land Use Cover (LUC) maps first appeared. Interest in this subject has remained strong since then because of the valuable information that these datasets provide for monitoring forests, deforestation and climate change, among other issues. A wide variety of thematic LUC datasets characterizing vegetation covers are currently available. In this chapter, we review eleven of these datasets, most of which provide long series of LUC maps, so permitting the study of LUC change. In thematic terms, m
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Olukolu, Bode A., and G. Craig Yencho. "Evolution of Molecular Marker Use in Cultivated Sweetpotato." In Compendium of Plant Genomes. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-65003-1_4.

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AbstractThe use of molecular markers in sweetpotato spans first, second, and the more recent NGS-based (next-generation sequencing) third-generation platforms. This attests to the long-term interest in sweetpotato as an economically important crop. The six homoeologous chromosomes of sweetpotato lead to complex inheritance patterns that require accurate estimation of allele dosage. The use of NGS for dosage-based genotyping marked a significant advancement in sweetpotato research. Analytical pipelines have emerged to handle dosage-based genotype datasets that account for complex patterns of in
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Borwankar, Saumya, Jaynil Shah, Sai Rohith, Deepankur Kansal, N. Ganesh Rohit, and Jai Prakash Verma. "Which Wine is that? A NLP Approach to Wine Variety Detection." In New Frontiers in Communication and Intelligent Systems. Soft Computing Research Society, 2021. http://dx.doi.org/10.52458/978-81-95502-00-4-17.

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Detecting wine variety based on the country, year of origin and the review alone is a very difficult task. This problem is converted into a classification problem using the combination of Natural Language Processing (NLP) and machin learning. A public dataset that consisted of wine variety corresponding to its country, re view, designation, province, winery and year was used for analysis of proposed approach. Natural Language Processing is used as a preprocessing step in our ap proach along with neural network to build a classifier. For more robust training k-fold cross validation technique wa
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Seker, Abdulkadir, Banu Diri, Halil Arslan, and Mehmet Fatih Amasyalı. "Open Source Software Development Challenges." In Research Anthology on Usage and Development of Open Source Software. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-9158-1.ch003.

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GitHub is the most common code hosting and repository service for open-source software (OSS) projects. Thanks to the great variety of features, researchers benefit from GitHub to solve a wide range of OSS development challenges. In this context, the authors thought that was important to conduct a literature review on studies that used GitHub data. To reach these studies, they conducted this literature review based on a GitHub dataset source study instead of a keyword-based search in digital libraries. Since GHTorrent is the most widely known GitHub dataset according to the literature, they con
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Seker, Abdulkadir, Banu Diri, Halil Arslan, and Mehmet Fatih Amasyalı. "Open Source Software Development Challenges." In Research Anthology on Agile Software, Software Development, and Testing. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3702-5.ch102.

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GitHub is the most common code hosting and repository service for open-source software (OSS) projects. Thanks to the great variety of features, researchers benefit from GitHub to solve a wide range of OSS development challenges. In this context, the authors thought that was important to conduct a literature review on studies that used GitHub data. To reach these studies, they conducted this literature review based on a GitHub dataset source study instead of a keyword-based search in digital libraries. Since GHTorrent is the most widely known GitHub dataset according to the literature, they con
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Bahadur, Preeti Singh, Jay Krishna Kumar, Shivanshu Dixit, Shubham Verma, Shubham Maurya, and Danish Jigar. "Review on Big Data Analytics Applications." In AI and the Revival of Big Data. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8472-5.ch005.

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Decision-makers in the age of digitization now have easy access to massive volumes of data. Big data are datasets that are challenging to manage with conventional tools and operations due to the fact that their operations are not only big but also extremely changeable and ever-changing. Owing to the exponential growth of this type of data, methods for managing and deriving insights and value from these datasets need to be generated and researched. Furthermore, decision-makers must be capable to draw insightful conclusions from a wide variety of quickly evolving data, including social network d
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Bhuvaneswari, Anbalagan, Swathi R., and Kalpalathika N. "Wind Data Pattern and Trend Analysis Using Feature Identification and Extreme Wind Speed Prediction." In Optimization Techniques for Hybrid Power Systems. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-0492-1.ch005.

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The most dangerous and destructive natural disasters in the world are wind-related. A literature review on machine learning-based approach is done for identification of wind disaster types and the forecasting of extreme wind speed. The study utilizes statistical techniques and machine learning models to uncover valuable insights into wind behavior and develop accurate predictions. A comprehensive dataset of wind speed and direction measurements is collected and preprocessed, ensuring data quality. Relevant features, including meteorological variables, geographical factors, and seasonal indicat
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Seethapathy, Bangaru Kamatchi, and Parvathi R. "A Review on Spatial Big Data Analytics and Visualization." In Modern Technologies for Big Data Classification and Clustering. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2805-0.ch007.

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Spatial dataset, which is becoming nontraditional due to the increase in usage of social media sensor networks, gaming and many other new emerging technologies and applications. The wide variety of sensors are used in solving real time problems like natural calamities, traffic analysis, analyzing climatic conditions and the usage of GPS, GPRS in mobile phones all together creates huge amount of spatial data which really exceeds the traditional spatial data analytics platform and become spatial big data .Spatial big data provide new demanding situations for their size, analysis, and exploration
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Conference papers on the topic "Wine Review Dataset"

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El-Attar, Noha E., and Yehia A. El-Mashad. "Artificial intelligence models for genomics analysis: review article." In Agria Média 2023 és ICI-17 Információ- és Oktatástechnológiai konferencia. Eszterházy Károly Katolikus Egyetem Líceum Kiadó, 2024. http://dx.doi.org/10.17048/am.2023.134.

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Artificial intelligence (AI) including machine learning (ML), and deep learning (DL) models have become powerful tools for analyzing genomics data in recent years. These models can process large amounts of data and identify complex patterns that may not be apparent through traditional statistical methods. ML and DL models have been used for a wide range of genomics applications, including gene expression analysis, variant detection, and drug discovery. One popular approach for using ML and DL models in genomics is to train these models on large datasets of genomic information. These datasets m
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Kaur, Rajinder, Ganesh Kumar Sethi, and Sartajvir Singh. "Review on Machine Learning-based Change Detection and Pan Sharpening Algorithms for Remote Sensing Datasets." In 2023 IEEE 9th International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE). IEEE, 2023. http://dx.doi.org/10.1109/wiecon-ece60392.2023.10456370.

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Wang, Junzhen, and Jianmin Qu. "Guided Waves-Based Disbond Detection of Double-Layer Plates using LSTM Networks." In 2024 51st Annual Review of Progress in Quantitative Nondestructive Evaluation. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/qnde2024-134721.

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Abstract Adhesively bonded structures are of great interest in a wide range of industries. However, such adhesive bonds are prone to interfacial defects like disbond and delamination during both the fabrication process and service life. In this paper, we propose a deep-learning (DL) approach to automatically localize and size the disbond in a double-layer plate using ultrasonic guided waves. This plate consists of an aluminum substrate with a stainless-steel coating layer. A guided wave active sensing procedure is used by implementing one transmitter-receiver configuration. Both guided wave pu
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Dakhil, Radhwan Adnan, and Ali Retha Hasoon Khayeat. "Review on Deep Learning Techniques for Underwater Object Detection." In 3rd International Conference on Data Science and Machine Learning (DSML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121505.

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Repair and maintenance of underwater structures as well as marine science rely heavily on the results of underwater object detection, which is a crucial part of the image processing workflow. Although many computer vision-based approaches have been presented, no one has yet developed a system that reliably and accurately detects and categorizes objects and animals found in the deep sea. This is largely due to obstacles that scatter and absorb light in an underwater setting. With the introduction of deep learning, scientists have been able to address a wide range of issues, including safeguardi
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Denaux, Ronald, Martino Mensio, Jose Manuel Gomez-Perez, and Harith Alani. "Weaving a Semantic Web of Credibility Reviews for Explainable Misinformation Detection (Extended Abstract)." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/646.

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This paper summarises work where we combined semantic web technologies with deep learning systems to obtain state-of-the art explainable misinformation detection. We proposed a conceptual and computational model to describe a wide range of misinformation detection systems based around the concepts of credibility and reviews. We described how Credibility Reviews (CRs) can be used to build networks of distributed bots that collaborate for misinformation detection which we evaluated by building a prototype based on publicly available datasets and deep learning models.
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Wang, Ya Nan, and Jun Wang. "Create a Win-Win Situation between the Knowledge Diffusion and the Benefits by Placing the Cost at the Threshold." In The 6th International Conference on Numerical Modelling in Engineering. Trans Tech Publications Ltd, 2024. http://dx.doi.org/10.4028/p-n3mxvc.

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Collaboration on knowledge is an essential channel for fostering the production and integration of knowledge. Knowledge collaboration user interactions can evolve into a network for knowledge collaboration. The "resources" variable has a significant effect on knowledge diffusion in the actual world. This paper examines the impact of resource production and consumption processes on the knowledge diffusion. We construct the knowledge diffusion model and determine the threshold for knowledge diffusion's propagation. We analyze the existing collaboration network dataset, Erdos Collaboration Networ
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Hashemi, Mohammad, Shengbo Gong, Juntong Ni, Wenqi Fan, B. Aditya Prakash, and Wei Jin. "A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/891.

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Many real-world datasets can be naturally represented as graphs, spanning a wide range of domains. However, the increasing complexity and size of graph datasets present significant challenges for analysis and computation. In response, graph reduction techniques have gained prominence for simplifying large graphs while preserving essential properties. In this survey, we aim to provide a comprehensive understanding of graph reduction methods, including graph sparsification, graph coarsening, and graph condensation. Specifically, we establish a unified definition for these methods and introduce a
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Guzman, Rel. "Monte Carlo Methods on High Dimensional Data." In LatinX in AI at Neural Information Processing Systems Conference 2018. Journal of LatinX in AI Research, 2018. http://dx.doi.org/10.52591/lxai2018120314.

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Markov Chain Monte Carlo (MCMC) simulation is a family of stochastic algorithms that are commonly used to approximate probability distributions by generating samples. The aim of this proposal is to deal with the problem of doing that job on a large scale because due to the increasing power computational demands of data being tall or wide, a study that combines statistical and engineering expertise can be made in order to achieve hardware-accelerated MCMC inference. In this work, I attempt to advance the theory and practice of approximate MCMC methods by developing a toolbox of distributed MCMC
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Makri, Isavella M., Stephen M. Rose, Marios Christou, Richard Gibson, and Graham Feld. "Examining Field Measurements of Deep-Water Crest Statistics." In ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/omae2016-54363.

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This paper concerns the field measurements of wave crest elevations and wind speed obtained from fixed platforms in the North Sea during the period of 2011 to 2015. An improved quality control process is proposed by the authors aiming to maximise the amount of waves that are not discarded, whilst maintaining a reliable database. Applying this revised methodology, the total number of waves in the quality-controlled dataset is approximately 184 million. In total, the measurements correspond to 32 years of continuous data and contain approximately 0.0054% of rogue waves, according to the definiti
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Kataeva, Veronika, and Maria Khodorchenko. "Attention-based estimation of topic model quality." In INTERNATIONAL CONFERENCE on Computational Linguistics and Intellectual Technologies. RSUH, 2023. http://dx.doi.org/10.28995/2075-7182-2023-22-215-224.

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Topic modeling is an essential instrument for exploring and uncovering latent patterns in unstructured textual data, that allows researchers and analysts to extract valuable understanding of a particular domain. Nonetheless, topic modeling lacks consensus on the matter of its evaluation. The estimation of obtained insightful topics is complicated by several obstacles, the majority of which are summarized by the absence of a unified system of metrics, the one-sidedness of evaluation, and the lack of generalization. Despite various approaches proposed in the literature, there is still no consens
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Reports on the topic "Wine Review Dataset"

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Stuedlein, Armin, Ali Dadashiserej, and Amalesh Jana. Models for the Cyclic Resistance of Silts and Evaluation of Cyclic Failure during Subduction Zone Earthquakes. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, 2023. http://dx.doi.org/10.55461/zkvv5271.

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This report describes several advances in the cyclic failure assessment of silt soils with immediate and practical benefit to the geotechnical earthquake engineering profession. First, a database of cyclic loading test data is assembled, evaluated, and used to assess trends in the curvature of the CRR-N (cyclic resistance ratio - the number of equivalent cycles) relationship. This effort culminated in a plasticity index-dependent function which can be used to estimate the exponent b in the power law describing cyclic resistance, and may be used to estimate the cyclic resistance of silt soils a
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Langlais, Pierre-Carl. Scientific Integrity. Comité pour la science ouverte, 2024. https://doi.org/10.52949/59.

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Between 2-4% of researchers admit to have falsified or fabricated their data. The prevalence of such unethical behavior can be as high as 10% in some disciplines or countries. Data falsification is an extreme form of questionable research practices that are both less problematic and much more widespread: surveys on different disciplines have shown that more than half of researchers make some form of selective reporting or add new data until they obtain significant results. Unethical practices harm the global quality of research. Provided they have been validated by peer review, fabricated, dis
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Michalak, Julia, Josh Lawler, John Gross, and Caitlin Littlefield. A strategic analysis of climate vulnerability of national park resources and values. National Park Service, 2021. http://dx.doi.org/10.36967/nrr-2287214.

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The U.S. national parks have experienced significant climate-change impacts and rapid, on-going changes are expected to continue. Despite the significant climate-change vulnerabilities facing parks, relatively few parks have conducted comprehensive climate-change vulnerability assessments, defined as assessments that synthesize vulnerability information from a wide range of sources, identify key climate-change impacts, and prioritize vulnerable park resources (Michalak et al. In review). In recognition that funding and planning capacity is limited, this project was initiated to identify geogra
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Corriveau, L., J. F. Montreuil, O. Blein, et al. Metasomatic iron and alkali calcic (MIAC) system frameworks: a TGI-6 task force to help de-risk exploration for IOCG, IOA and affiliated primary critical metal deposits. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329093.

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Australia's and China's resources (e.g. Olympic Dam Cu-U-Au-Ag and Bayan Obo REE deposits) highlight how discovery and mining of iron oxide copper-gold (IOCG), iron oxide±apatite (IOA) and affiliated primary critical metal deposits in metasomatic iron and alkali-calcic (MIAC) mineral systems can secure a long-term supply of critical metals for Canada and its partners. In Canada, MIAC systems comprise a wide range of undeveloped primary critical metal deposits (e.g. NWT NICO Au-Co-Bi-Cu and Québec HREE-rich Josette deposits). Underexplored settings are parts of metallogenic belts that extend in
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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, 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 bas
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6

Allen, Kathy, Andy Nadeau, and Andy Robertston. Natural resource condition assessment: Salinas Pueblo Missions National Monument. National Park Service, 2022. http://dx.doi.org/10.36967/nrr-2293613.

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The Natural Resource Condition Assessment (NRCA) Program aims to provide documentation about the current conditions of important park natural resources through a spatially explicit, multi-disciplinary synthesis of existing scientific data and knowledge. Findings from the NRCA will help Salinas Pueblo Missions National Monument (SAPU) managers to develop near-term management priorities, engage in watershed or landscape scale partnership and education efforts, conduct park planning, and report program performance (e.g., Department of the Interior’s Strategic Plan “land health” goals, Government
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