Relevant bibliographies by topics / Co-Evolving data

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

Academic literature on the topic 'Co-Evolving data'

Author: Grafiati
Published: 30 November 2024
Last updated: 31 July 2025

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Journal articles on the topic "Co-Evolving data"

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Wang, Yi, and Tao Li. "Improving semi-supervised co-forest algorithm in evolving data streams." Applied Intelligence 48, no. 10 (2018): 3248–62. http://dx.doi.org/10.1007/s10489-018-1149-7.

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Praczyk, Tomasz. "Evolving Co-Adapted Subcomponents in Assembler Encoding." International Journal of Applied Mathematics and Computer Science 17, no. 4 (2007): 549–63. http://dx.doi.org/10.2478/v10006-007-0045-9.

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Evolving Co-Adapted Subcomponents in Assembler EncodingThe paper presents a new Artificial Neural Network (ANN) encoding method called Assembler Encoding (AE). It assumes that the ANN is encoded in the form of a program (Assembler Encoding Program, AEP) of a linear organization and of a structure similar to the structure of a simple assembler program. The task of the AEP is to create a Connectivity Matrix (CM) which can be transformed into the ANN of any architecture. To create AEPs, and in consequence ANNs, genetic algorithms (GAs) are used. In addition to the outline of AE, the paper also pr
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Deng, Jinliang, Xiusi Chen, Zipei Fan, Renhe Jiang, Xuan Song, and Ivor W. Tsang. "The Pulse of Urban Transport: Exploring the Co-evolving Pattern for Spatio-temporal Forecasting." ACM Transactions on Knowledge Discovery from Data 15, no. 6 (2021): 1–25. http://dx.doi.org/10.1145/3450528.

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Transportation demand forecasting is a topic of large practical value. However, the model that fits the demand of one transportation by only considering the historical data of its own could be vulnerable since random fluctuations could easily impact the modeling. On the other hand, common factors like time and region attribute, drive the evolution demand of different transportation, leading to a co-evolving intrinsic property between different kinds of transportation. In this work, we focus on exploring the co-evolution between different modes of transport, e.g., taxi demand and shared-bike de
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Yuan, Yinyin. "Abstract IA006: Co-evolving artificial intelligence and pathology." Cancer Prevention Research 15, no. 12_Supplement_1 (2022): IA006. http://dx.doi.org/10.1158/1940-6215.dcis22-ia006.

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Abstract A fundamental question in cancer biology is how cancers evolve heterogeneity and treatment resistance. The evolution trajectory of cancer is dictated by selective pressures from treatments and the tumor ecosystem. Artificial intelligence (AI) enables us to directly study geographical patterns of the microenvironment in pathological samples, to infer cancer habitats and niches. Significant challenges and open questions remain: how to establish multidisciplinary platforms, develop reproducible AI tools, and how to leverage pathology, genetic, molecular, and clinical data to improve pers
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Hengpraprohm, Supoj, Kairung Hengpraprohm, Dech Thammasiri, and Suvimol Mukviboonchai. "Co-Evolving Ensemble of Genetic Algorithm Classifier for Cancer Microarray Data Classification." Advanced Science Letters 24, no. 2 (2018): 1330–33. http://dx.doi.org/10.1166/asl.2018.10743.

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Khouri, Selma, and Ladjel Bellatreche. "LOD for Data Warehouses: Managing the Ecosystem Co-Evolution." Information 9, no. 7 (2018): 174. http://dx.doi.org/10.3390/info9070174.

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For more than 30 years, data warehouses (DWs) have attracted particular interest both in practice and in research. This success is explained by their ability to adapt to their evolving environment. One of the last challenges for DWs is their ability to open their frontiers to external data sources in addition to internal sources. The development of linked open data (LOD) as external sources is an excellent opportunity to create added value and enrich the analytical capabilities of DWs. However, the incorporation of LOD in the DW must be accompanied by careful management. In this paper, we are
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Clayson, Amanda, Lucy Webb, and Nigel Cox. "When two worlds collide: critical reflection on co-production." Drugs and Alcohol Today 18, no. 1 (2018): 51–60. http://dx.doi.org/10.1108/dat-08-2017-0040.

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Purpose The purpose of this paper is to report the findings from reflexive data collection on the evolving co-production research relationship between the two “worlds” of community and academia: people with lived experience and their community intermediaries and academic researchers. It reports analysis of reflections on experience as the different partners explore and evaluate their own experiences of co-productive research within the context of substance use recovery co-production research. Design/methodology/approach The research uses reflexive data from perspectives of an intermediary comm
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Judijanto, Loso, Adi Suroso, and Andriya Risdwiyanto. "Bibliometric Assessment of Data-Driven Marketing Research Trends in the Last Two Decades." West Science Business and Management 2, no. 03 (2024): 987–1001. http://dx.doi.org/10.58812/wsbm.v2i03.1278.

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This study presents a bibliometric analysis of data-driven marketing research trends over the last two decades, with a focus on its intersection with innovation, decision-making, and related topics. Using VOSviewer for network visualization, we analyze co-authorship, keyword co-occurrence, publication frequency, and country collaboration to uncover key themes and research developments. The findings indicate a significant rise in data-driven marketing research, particularly from 2015 onwards, driven by advancements in big data, machine learning, and artificial intelligence. Co-authorship networ
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Mohd Amran Mohd Daril, Fozia Fatima, Samar Raza Talpur, and Alhamzah F. Abbas. "Unveiling the Landscape of Big Data Analytics in Healthcare: A Comprehensive Bibliometric Analysis." International Journal of Online and Biomedical Engineering (iJOE) 20, no. 06 (2024): 4–24. http://dx.doi.org/10.3991/ijoe.v20i06.48085.

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In the rapidly evolving landscape of healthcare, the digital transformation marked by healthcare 4.0 has spurred a surge in data generation, giving rise to ‘big data’. Big data analytics has become an effective tool in the healthcare industry, revolutionising medical research, patient care, and healthcare management. This study undertakes a meticulous bibliometric analysis, drawing upon a dataset of 2212 articles from the Scopus database spanning 2014 to 2023, to unravel the trajectory of big data analytics in healthcare. The research explores diverse dimensions, from the distribution of studi
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Yasaka, Noriaki. "Data mining in anti-money laundering field." Journal of Money Laundering Control 20, no. 3 (2017): 301–10. http://dx.doi.org/10.1108/jmlc-09-2015-0041.

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Purpose This report aims to focus on how suspicious transaction report is created with data mining methods and used from the point of view of knowledge management. Design/methodology/approach This paper considers data mining versus knowledge management in the anti-money laundering (AML) field. Findings In the AML field, the information and knowledge gained are not necessarily used for or shared with the related shareholders. Creating and co-evolving the network of “knowledge professionals” is the impending assignment in this industry. The first and most important task is knowledge management i
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Dissertations / Theses on the topic "Co-Evolving data"

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Li, Lei. "Fast Algorithms for Mining Co-evolving Time Series." Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/112.

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Time series data arise in many applications, from motion capture, environmental monitoring, temperatures in data centers, to physiological signals in health care. In the thesis, I will focus on the theme of learning and mining large collections of co-evolving sequences, with the goal of developing fast algorithms for finding patterns, summarization, and anomalies. In particular, this thesis will answer the following recurring challenges for time series: 1. Forecasting and imputation: How to do forecasting and to recover missing values in time series data? 2. Pattern discovery and summarization
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Owusu, Patrick Asante. "Modélisation de dépendances dans des séries temporelles co-évolutives." Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0104.

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Les recherches actuelles en analyse des séries temporelles montrent qu'il existe des approches formelles insuffisantes pour modéliser les dépendances de plusieurs séries temporelles ou de séries temporelles co-évolutives à mesure qu'elles changent au fil du temps. Dans cette thèse, nous développons une approche formelle pour analyser la temporalité et l'évolution des dépendances via les définitions de sous-séries temporelles, où une sous-série temporelle est un segment des données de la série temporelle originale. Nous concevons une approche basée sur le principe des fenêtres glissantes pour a
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"Studies on agent-based co-evolving networks." 2012. http://library.cuhk.edu.hk/record=b5549195.

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本論文包含四個部分。每一部分我們演示一個在共同演化網絡中的個體為本(agent-based)模型。第二章是不滿適應雪堆博奕(DASG)的廣泛化。第三章是自省適應(self-questioning adaptive)雪堆博奕。第四章是共同演化選民模型的廣泛化。第五章是有三個互相克制的角色的適應性石頭-布-剪刀(ARPS)模型。在這些模型中，適應行為導致共同演化過程發生。我們以電算模擬及理論方法研究這些模型。我們的目標是建立一個可應用於不同共同演化網絡的一般分析框架。<br>在第二章及第四章，我們將Gräser等人的DASG及Vazquez等人的共同演化選民模型從一個控制參數推廣到二個獨立的控制參數。在他們的工作中，根據網絡的結構定義了一些相，而且發展了平均場理論。而在廣泛化的情況下，在已伸延的相空間上，我們也定義了一些相及發展了一些廣泛化的平均場理論。在廣泛化DASG中，我們以考慮在相邊界附近的最終生存形態(last surviving patterns)以解釋相邊界的電算模擬結果。<br>在第三章，我們提出及研究一個以誘惑驅動的雪堆博奕。該更新機制被稱為自省機制(self-questioning mechanism)。我們給出模擬及理論結果，也討論了該些結果的物理意義。<br>在第五章，我們推廣我們的研究至有三個策略的遊戲。我們提出及研究了一個ARPS模型，其中每個個體採用三個互相
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Books on the topic "Co-Evolving data"

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Matsumi, Hideyuki, Dara Hallinan, Diana Dimitrova, Eleni Kosta, and Paul De Hert, eds. Data Protection and Privacy, Volume 16. Hart Publishing, 2024. http://dx.doi.org/10.5040/9781509975976.

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This book explores the complexity and depths of our digital world by providing a selection of analyses and discussions from the 16th annual international conference on Computers, Privacy and Data Protection (CPDP): Ideas that Drive Our Digital World. The first half of the book focuses on issues related to the GDPR and data. These chapters provide a critical analysis of the 5-year history of the complex GDPR enforcement system, covering: codes of conduct as a potential co-regulation instrument for the market; an interdisciplinary approach to privacy assessment on synthetic data; the ethical imp
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Carvalho, André F., and Eduard Vieta, eds. The Treatment of Bipolar Disorder. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198748625.001.0001.

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Bipolar disorder is a chronic and debilitating mental illness affecting a significant proportion of the world’s population. It is associated with significant impairments in health-related quality of life and psychosocial functioning, and has significant illness-related morbidity and heightened mortality rates due to medical co-morbidities and suicide. The management of this disorder requires a complex combination of pharmacological and psychosocial interventions which can be challenging for clinicians. This book provides readers with a concise and comprehensive guide to the integrative managem
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Book chapters on the topic "Co-Evolving data"

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Xiao, Hongmei, Xiuli Ma, Shiwei Tang, and Chunhua Tian. "Continuous Summarization of Co-evolving Data in Large Water Distribution Network." In Web-Age Information Management. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14246-8_9.

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Cheng, Yun, Xiucheng Li, and Yan Li. "Finding Dynamic Co-evolving Zones in Spatial-Temporal Time Series Data." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46131-1_20.

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Shapiro, J. L. "Does data-model co-evolution improve generalization performance of evolving learners?" In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0056896.

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Detterer, Dion, and Paul Kwan. "COW: A Co-evolving Memetic Wrapper for Herb-Herb Interaction Analysis in TCM Informatics." In New Frontiers in Applied Data Mining. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28320-8_31.

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Broneske, David, Vitalii Burtsev, Anna Drewes, Bala Gurumurthy, Thilo Pionteck, and Gunter Saake. "ADAMANT: Hardware-Accelerated Query Processing Made Easy." In Scalable Data Management for Future Hardware. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-74097-8_1.

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Abstract We present ADAMANT, an adaptive data management architecture for evolving heterogeneous hardware/software systems. The ADAMANT system enables plug’n’play integration of co-processors such as GPUs and FPGAs, provides a unified runtime that supports cross-device parallelization of SQL query execution on arbitrary co-processors at runtime, and supports multi-query processing. We discuss the concepts behind and performance of ADAMANT following an example query (TPC-H Q6), present different execution models for cross-device execution, and provide an FPGA hardware architecture that allows t
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Bates, Jo, Alessandro Checco, and Elli Gerakopoulou. "Worker Perspectives on Designs for a Crowdwork Co-operative." In Transforming Communications – Studies in Cross-Media Research. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96180-0_18.

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AbstractCrowdwork platforms such as Amazon Mechanical Turk (AMT) are a crucial infrastructural component of our global data assemblage. Through these platforms, low-paid crowdworkers perform the vital labour of manually labelling large-scale and complex datasets, labels that are needed to train machine learning and AI models (Tubaro et al., Big Data &amp; Society, 7(1), 2020) and which enable the functioning of much digital technology, from niche applications to global platforms such as Google, Amazon and Facebook.In this chapter, we reflect on how a ‘design justice’ approach might be valuable
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Phan, Thomas, Kavitha Ranganathan, and Radu Sion. "Evolving Toward the Perfect Schedule: Co-scheduling Job Assignments and Data Replication in Wide-Area Systems Using a Genetic Algorithm." In Job Scheduling Strategies for Parallel Processing. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11605300_9.

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Jintrawet, Attachai, and Kono Yasuyuki. "Current Situation and Future of Precision Agriculture in Thailand." In Countries and Regions. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2835-0_7.

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AbstractThe traditional agricultural research and improvement framework, based on the reductionism paradigm, that form the conceptual basis of agriculture today, are insufficient to address the demands of various actors. Precision Agriculture (PA), paradigms and technologies, have their origins in improving the efficiency of farm-level agricultural resource management based on understanding-driven/data-driven paradigm (UDP), especially modeling and decision support systems (DSS). DSS can be further developed into PA Simulators and are relevant to agricultural systems in a small-farm context. U
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Tan, Bowen, Shibo Hao, Eric Xing, and Zhiting Hu. "Chapter 7. Neural-Symbolic Interaction and Co-Evolving." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230139.

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Symbolic knowledge is vital in various human cognitive functions, including reasoning, skill acquisition, and communication. Its integration is also essential for creating AI with human-like capabilities, such as robustness, creativity, and interpretability. Nevertheless, current machine learning approaches still dominantly emphasize learning from large data sets, struggling to effectively and scalably incorporate symbolic knowledge. This leads to fundamental limitations, such as brittle results when faced with complex or novel concepts, and difficulty in understanding or explaining the decisi
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Marks, R., D. Midgley, and L. Cooper. "Co-Evolving Better Strategies in Oligopolistic Price Wars." In Handbook of Research on Nature-Inspired Computing for Economics and Management. IGI Global, 2007. http://dx.doi.org/10.4018/978-1-59140-984-7.ch052.

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Using empirical market data from brand rivalry in a retail ground-coffee market, we model each idiosyncratic brand’s pricing behavior using the restriction that marketing strategies depend only on profit-relevant state variables, and use the genetic algorithm to search for co-evolved equilibria, where each profit-maximizing brand manager is a stimulus-response automaton, responding to past prices in the asymmetric oligopolistic market. This chapter is part of a growing study of repeated interactions and oligopolistic behavior using the GA.
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Conference papers on the topic "Co-Evolving data"

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Yoo, Jin Soung, Shashi Shekhar, Sangho Kim, and Mete Celik. "Discovery of Co-evolving Spatial Event Sets." In Proceedings of the 2006 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2006. http://dx.doi.org/10.1137/1.9781611972764.27.

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Matsubara, Yasuko, Yasushi Sakurai, Naonori Ueda, and Masatoshi Yoshikawa. "Fast and Exact Monitoring of Co-Evolving Data Streams." In 2014 IEEE International Conference on Data Mining (ICDM). IEEE, 2014. http://dx.doi.org/10.1109/icdm.2014.62.

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Kimura, Tasuku, Yasuko Matsubara, Koki Kawabata, and Yasushi Sakurai. "Fast Mining and Forecasting of Co-evolving Epidemiological Data Streams." In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2022. http://dx.doi.org/10.1145/3534678.3539078.

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Moran, Alejandro, Vincent Canals, Plamen P. Angelov, et al. "Stochastic Computing co-processing elements for Evolving Autonomous Data Partitioning." In 2021 XXXVI Conference on Design of Circuits and Integrated Systems (DCIS). IEEE, 2021. http://dx.doi.org/10.1109/dcis53048.2021.9666167.

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Ma, Yunqiang, Junli Lu, and Dazhi Yang. "Mining Evolving Spatial Co-location Patterns from Spatio-temporal Databases." In 2022 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2022. http://dx.doi.org/10.1109/bigcomp54360.2022.00034.

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Cazzola, Walter, Sonia Pini, Ahmed Ghoneim, and Gunter Saake. "Co-evolving application code and design models by exploiting meta-data." In the 2007 ACM symposium. ACM Press, 2007. http://dx.doi.org/10.1145/1244002.1244278.

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Pillai, Karthik Ganesan, Rafal A. Angryk, Juan M. Banda, Michael A. Schuh, and Tim Wylie. "Spatio-temporal Co-occurrence Pattern Mining in Data Sets with Evolving Regions." In 2012 IEEE 12th International Conference on Data Mining Workshops. IEEE, 2012. http://dx.doi.org/10.1109/icdmw.2012.130.

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Gu, Yupeng, Yizhou Sun, and Jianxi Gao. "The Co-Evolution Model for Social Network Evolving and Opinion Migration." In KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017. http://dx.doi.org/10.1145/3097983.3098002.

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Tajeuna, Etienne Gael, and Mohamed Bouguessa. "Dynamic Cox-Regression for Motif Prediction in Co-Evolving Time Series Data." In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. http://dx.doi.org/10.1109/ijcnn55064.2022.9892174.

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Eicken, Hajo, Olivia A. Lee, and Amy L. Lovecraft. "Evolving roles of observing systems and data co-management in Arctic Ocean governance." In OCEANS 2016 MTS/IEEE Monterey. IEEE, 2016. http://dx.doi.org/10.1109/oceans.2016.7761298.

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Reports on the topic "Co-Evolving data"

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Yi, B. K., N. D. Sidiropoulos, T. Johnson, H. V. Jagadish, and C. Faloutsos. Online Data Mining for Co-Evolving Time Sequences. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada371154.

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Bernardo, Allan, Jose Ramon Albert, Jana Flor Vizmanos, and Mika Muñoz. Toward Measuring Soft Skills for Youth Development: A Scoping Study. Philippine Institute for Development Studies, 2023. http://dx.doi.org/10.62986/dp2023.28.

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Rapid technological advancements and shifting economic paradigms in the 21st century also continuously change the nature of work, wherein more complex and sophisticated skill sets are required. There is a growing recognition of soft skills' pivotal role in preparing the youth for this evolving environment. However, a notable gap remains in identifying what comprises these soft skills or Transversal Competencies (TVC). The study aims to contribute to policy discussions to support the government in enhancing its understanding of soft skills building and formulating strategies to cultivate a well
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