Academic literature on the topic 'Co-Evolving data'
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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 (February 20, 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 (December 1, 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 presents a new AEP encoding method, i.e., the method used to represent the AEP in the form of a chromosome or a set of chromosomes. The proposed method assumes the evolution of individual components of AEPs, i.e., operations and data, in separate populations. To test the method, experiments in two areas were carried out, i.e., in optimization and in a predator-prey problem. In the first case, the task of AE was to create matrices which constituted a solution to the optimization problem. In the second case, AE was responsible for constructing neural controllers used to control artificial predators whose task was to capture a fast-moving prey.
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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 (May 19, 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 demand. Two significant challenges impede the discovery of the co-evolving pattern: (1) diversity of the co-evolving correlation, which varies from region to region and time to time. (2) Multi-modal data fusion. Taxi demand and shared-bike demand are time-series data, which have different representations with the external factors. Moreover, the distribution of taxi demand and bike demand are not identical. To overcome these challenges, we propose a novel method, known as co-evolving spatial temporal neural network (CEST). CEST learns a multi-view demand representation for each mode of transport, extracts the co-evolving pattern, then predicts the demand for the target transportation based on multi-scale representation, which includes fine-scale demand information and coarse-scale pattern information. We conduct extensive experiments to validate the superiority of our model over the state-of-art models.
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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 (February 1, 2018): 1330–33. http://dx.doi.org/10.1166/asl.2018.10743.
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Yuan, Yinyin. "Abstract IA006: Co-evolving artificial intelligence and pathology." Cancer Prevention Research 15, no. 12_Supplement_1 (December 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 personalized oncology. Charles Darwin described how the intimate coexistence between flowering plants and insects leads to reciprocal evolutionary changes; this is now known as coevolution. Today, through the demolition of disciplinary barriers, AI and pathology can co-evolve to create evolutionary changes and new paradigms. I will discuss our latest progress on combining AI and experimental technologies for spatial histology and omics data analysis. We aim to understand how cancer evolves within diverse environmental conditions. Our work has revealed a high level of geospatial variation in the tumor microenvironment, with profound implications for early diagnosis, biomarker development, and cancer therapeutics. Citation Format: Yinyin Yuan. Co-evolving artificial intelligence and pathology [abstract]. In: Proceedings of the AACR Special Conference on Rethinking DCIS: An Opportunity for Prevention?; 2022 Sep 8-11; Philadelphia, PA. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_1): Abstract nr IA006.
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Khouri, Selma, and Ladjel Bellatreche. "LOD for Data Warehouses: Managing the Ecosystem Co-Evolution." Information 9, no. 7 (July 17, 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 interested in managing the evolution of DW systems integrating internal and external LOD datasets. The particularity of LOD is that they contribute to evolving the DW at several levels: (i) source level, (ii) DW schema level, and (iii) DW design-cycle constructs. In this context, we have to ensure this co-evolution, as conventional evolution approaches are adapted neither to this new kind of source nor to semantic constructs underlying LOD sources. One way of tackling this co-evolution issue is to ensure the traceability of DW constructs for the whole design cycle. Our approach is tested using: the LUBM (Lehigh University BenchMark), different LOD datasets (DBepedia, YAGO, etc.), and Oracle 12c database management system (DBMS) used for the DW deployment.
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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 (March 5, 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 community partner, academic partners, and community researchers on experiences of a series of co-productive research projects. The aim is to identify thematic features of the co-productive experiences from different positions and through the process of adaptation to a co-productive relationship. Findings This paper outlines what has been learnt from the experience of co-production and what has “worked” for community and academic partners; around the nature of co-production, barriers to performance, and its value to participants and the wider recovery research agenda. Originality/value This paper reports a unique perspective on a developing methodology in health and social care, contributing to a growing body of knowledge pertaining to experiences of co-production research.
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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 (April 12, 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 studies across years to the productivity rankings of journals, countries, and institutions, elucidating the evolving trends and key contributors. Co-authorship networks and keyword co-occurrence analysis reveal thematic clusters and intellectual structures, contributing to a nuanced understanding of the field. The results underscore the escalating global interest in the fusion of big data and healthcare, illuminating collaborations, and identifying influential players. Additionally, the study identifies pressing challenges, including security concerns and skill shortages, emphasizing the imperative of overcoming these barriers for effective big data applications in healthcare. Serving as a valuable resource for researchers, practitioners, and policymakers, this research not only captures the current landscape but also provides insights for future exploration, contributing to strategic planning in this dynamic domain.
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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 (September 30, 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 networks reveal strong interdisciplinary collaboration, while keyword co-occurrence maps highlight the growing role of innovation, decision-making, and machine learning in data-driven marketing. Additionally, country collaboration networks show the United States, China, the United Kingdom, and India as central contributors to global research. The keyword density heatmap emphasizes the increasing focus on data-driven innovation and product development. These insights offer valuable implications for academics and practitioners seeking to understand and apply data-driven marketing in a rapidly evolving digital landscape.
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Yasaka, Noriaki. "Data mining in anti-money laundering field." Journal of Money Laundering Control 20, no. 3 (July 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 in the global AML field. Originality/value The report considers the creation with data mining methods and utilization from the point of view of knowledge management.
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: How to identify the patterns in the time sequences that would facilitate further mining tasks such as compression, segmentation and anomaly detection?
3. Similarity and feature extraction: How to extract compact and meaningful features from multiple co-evolving sequences that will enable better clustering and similarity queries of time series?
4. Scale up: How to handle large data sets on modern computing hardware?
We develop models to mine time series with missing values, to extract compact representation from time sequences, to segment the sequences, and to do forecasting. For large scale data, we propose algorithms for learning time series models, in particular, including Linear Dynamical Systems (LDS) and Hidden Markov Models (HMM). We also develop a distributed algorithm for finding patterns in large web-click streams. Our thesis will present special models and algorithms that incorporate domain knowledge. For motion capture, we will describe the natural motion stitching and occlusion filling for human motion. In particular, we provide a metric for evaluating the naturalness of motion stitching, based which we choose the best stitching. Thanks to domain knowledge (body structure and bone lengths), our algorithm is capable of recovering occlusions in mocap sequences, better in accuracy and longer in missing period. We also develop an algorithm for forecasting thermal conditions in a warehouse-sized data center. The forecast will help us control and manage the data center in a energy-efficient way, which can save a significant percentage of electric power consumption in data centers.
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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 analyser la nature temporelle et les changements de dépendance entre les séries temporelles évolutives. Plus précisément, chaque sous-série temporelle est analysée indépendamment pour comprendre les dépendances locales et comment ces dépendances se déplacent à mesure que la fenêtre avance dans le temps. Cela nous permet donc de modéliser l'évolution temporelle des dépendances avec une granularité plus fine. Nos contributions relatives à la modélisation des dépendances soulignent l'importance de comprendre les interconnexions dynamiques entre plusieurs séries temporelles qui évoluent ensemble au fil du temps. L'objectif principal est de développer des techniques robustes qui peuvent capturer efficacement ces dépendances évolutives, améliorant ainsi l'analyse et la prévision de systèmes complexes tels que les marchés financiers, les systèmes climatiques et d'autres domaines générant de volumineuses données de séries temporelles. La thèse explore l'utilisation de modèles autorégressifs et propose des méthodes innovantes pour identifier et modéliser ces dépendances, en abordant les limitations des méthodes traditionnelles qui négligent souvent les dynamiques temporelles et l'évolutivité nécessaires pour gérer de grands ensembles de données. Un aspect central de la recherche est le développement d'une approche en deux étapes pour détecter et modéliser les effets évolutifs dans plusieurs séries temporelles. La première étape consiste à identifier des motifs pour recréer des variations de séries sur diverses périodes en utilisant des modèles linéaires finis. Cette étape est cruciale pour capturer les dépendances temporelles au sein des données. En exploitant une séquence de graphes bipartites, l'étude modélise le changement à travers plusieurs séries temporelles, reliant des dépendances répétitives et nouvelles à différentes durées temporelles dans les sous-séries. Cette approche simplifie non seulement le processus d'identification des dépendances, mais fournit également une solution évolutive pour analyser de grands ensembles de données, comme démontré par des expériences avec, par exemple, des données de marché financier du monde réel. La thèse souligne en outre l'importance de l'interprétabilité dans la modélisation des séries temporelles co-évolutives. En intégrant des grands modèles de langage (LLM) et des techniques contextuelles, la recherche améliore la compréhension des facteurs sous-jacents qui conduisent aux changements dans les données de séries temporelles. Cette interprétabilité est obtenue grâce à la construction de graphes temporels et à la sérialisation de ces graphes en langage naturel, fournissant des informations claires et complètes sur les dépendances et les interactions au sein des données. La combinaison de modèles autorégressifs et de LLM permet de générer des prévisions plausibles et interprétables, rendant l'approche adaptée aux applications du monde réel où la confiance et la clarté des résultats des modèles sont primordialesCurrent research in time series analysis shows that there are insufficient formal approaches for modelling the dependencies of multiple or co-evolving time series as they change over time. In this dissertation, we develop a formal approach for analysing the temporality and evolution of dependencies via the definitions of sub-time series, where a sub-time series is a segment of the original time series data. In general, we design an approach based on the principle of sliding windows to analyse the temporal nature and dependency changes between evolving time series. More precisely, each sub-time series is analysed independently to understand the local dependencies and how these dependencies shift as the window moves forward in time. This, therefore, allows us to model the temporal evolution of dependencies with finer granularity. Our contributions relating to the modelling of dependencies highlight the significance of understanding the dynamic interconnections between multiple time series that evolve together over time. The primary objective is to develop robust techniques to effectively capture these evolving dependencies, thereby improving the analysis and prediction of complex systems such as financial markets, climate systems, and other domains generating voluminous time series data. The dissertation explores the use of autoregressive models and proposes novel methods for identifying and modelling these dependencies, addressing the limitations of traditional methods that often overlook the temporal dynamics and scalability required for handling large datasets. A core aspect of the research is the development of a two-step approach to detect and model evolving effects in multiple time series. The first step involves identifying patterns to recreate series variations over various time intervals using finite linear models. This step is crucial for capturing the temporal dependencies within the data. By leveraging a sequence of bipartite graphs, the study models change across multiple time series, linking repetitive and new dependencies at varying time durations in sub-series. This approach not only simplifies the process of identifying dependencies but also provides a scalable solution for analysing large datasets, as demonstrated through experiments with, for example, real-world financial market data. The dissertation further emphasises the importance of interpretability in modelling co-evolving time series. By integrating large language models (LLMs) and context-aware techniques, the research enhances the understanding of the underlying factors driving changes in time series data. This interpretability is achieved through the construction of temporal graphs and the serialisation of these graphs into natural language, providing clear and comprehensive insights into the dependencies and interactions within the data. The combination of autoregressive models and LLMs enables the generation of plausible and interpretable predictions, making the approach suitable for real-world applications where trust and clarity in model outputs are paramount
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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。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。
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。
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克制的策略的其中之一。每個個體以概率 p來重連不理想的連結或以概率 (1 - p)改變自身的策略以適應其周遭環境。我們研究了網絡於不同的 p值在穩定態的行為及定義 了一些相。我們研究兩個選取重連對象的方法，分別對應於隨機選取及刻意選取重連對象，也解釋了得出的結果。我們在有關穩定勝利、平手及失敗概率的研究中及哪種個體可以有更高的勝利概率的研究中得出了有趣的結果。我們也研究了結果如何取決於初始條件。
在不同的模型中，理論方程均建立於相似的想法上。理論結果得出模擬結果的主要特性，包括出現了不同的相。該分析方法被證明了在本論文中對不同的模型也有效，而該方法也可被應用於很多其他共同演化網絡上。
This thesis consists of four parts. In each part, we present results of an agent-based model of co-evolving network. Chapter 2 deals with a generalization of the Dissatisfied Adaptive Snowdrift Game (DASG) and Chapter 3 covers the self-questioning adaptive snowdrift game. Chapter 4 discusses a generalization of a co-evolving voter model. Chapter 5 gives the results on a cyclic three-character Adaptive Rock-Paper-Scissor (ARPS) game. The adaptive actions give rise to co-evolving processes in these models. These models are studied both numerically and analytically. An objective here is to establish a general analytic framework that is applicable to different models of co-evolving networks.
In Chapters 2 and 4, we generalize two existing models -the DASG of Gräser et al. and the co-evolving voter model of Vazquez et al. -from a single control parameter to two independent parameters. Different phases were identified according to the network structure and mean-field theories were developed in the previous work. With the expanded phase space in our generalized models, we identified different phases and provided a generalized mean-field approach. The phase boundaries in the generalized DASG can be explained by considering the last surviving patterns in the vicinity of the transition between two phases.
In Chapter 3, we propose and study a co-evolving snowdrift game in which the adaptive actions are driven by the desire to enhance winning. The updating scheme is called the self-questioning mechanism. We present simulation and theoretical results, and provide physical meaning to the results.
In Chapter 5, we extend our study to three-strategy games. An ARPS model in which each agent uses one of three strategies that dominate each other cyclically is proposed and studied. Each agent adapts his local environment by rewiring an un-favourable link with a probability p or switching his strategy with a probability 1-p. As p varies, the behaviour of the network in the steady state is studied and dierent phases are identified. Two dierent schemes corresponding to selecting the rewiring target randomly and intentionally are studied and the results are explained. Interesting results are also found in the probabilities of winning, losing and drawing in the steady state; and the type of agents that have a higher winning probability. The dependence on the initial distribution of the three strategies among the agents is also studied.
The analytic equations presented for each model in the thesis are established on similar ideas. The analytic results capture the main features in the simulation results, including the emergence of dierent phases. The analytic approach, proven to be useful through different models in this thesis, can be applied to a wide class of other co-evolving network models.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Choi, Chi Wun / 個體為本共同演化網絡的研究 / 蔡至桓.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2012.
Includes bibliographical references (leaves 114-116).
Abstracts also in Chinese.
Choi, Chi Wun / Ge ti wei ben gong tong yan hua wang luo de yan jiu / Cai Zhihuan.
Abstract --- p.i
摘要 --- p.iii
Acknowledgements --- p.v
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Introduction --- p.1
Chapter 1.2 --- Review --- p.5
Chapter 1.2.1 --- Network and basic graph properties --- p.5
Chapter 1.2.2 --- Two-person games --- p.6
Chapter 2 --- Generalization of Dissatisfied-Adaptive Snowdrift Game (DASG) --- p.8
Chapter 2.1 --- Introduction --- p.8
Chapter 2.2 --- Dissatisfied-Adaptive model --- p.12
Chapter 2.3 --- Previous work --- p.14
Chapter 2.4 --- Generalized Dissatisfied-Adaptive model --- p.16
Chapter 2.5 --- Simulation results --- p.17
Chapter 2.6 --- Theoretical analysis --- p.19
Chapter 2.6.1 --- Mean-Field approach --- p.19
Chapter 2.6.2 --- Theoretical results --- p.22
Chapter 2.7 --- Last surviving patterns --- p.25
Chapter 2.7.1 --- Observing the last surviving patterns --- p.25
Chapter 2.7.2 --- Applying the theory using extracted information from simulations --- p.26
Chapter 2.7.3 --- Further development of the theory --- p.28
Chapter 2.7.4 --- Results of the theory --- p.30
Chapter 2.8 --- Dependence on initial conditions and mean degree --- p.32
Chapter 2.9 --- Conclusion --- p.34
Chapter 3 --- Self-questioning Adaptive SG --- p.36
Chapter 3.1 --- Introduction --- p.36
Chapter 3.2 --- Self-questioning adaptive SG with control parameter r --- p.39
Chapter 3.2.1 --- Model --- p.39
Chapter 3.2.2 --- Results --- p.40
Chapter 3.3 --- Self-questioning adaptive SG with control parameters r and h --- p.42
Chapter 3.3.1 --- Model --- p.42
Chapter 3.3.2 --- Results --- p.43
Chapter 3.4 --- Conclusion --- p.45
Chapter 4 --- Generalization of co-evolving voter model --- p.46
Chapter 4.1 --- Introduction --- p.46
Chapter 4.2 --- Co-evolving voter model --- p.49
Chapter 4.3 --- Previous work --- p.50
Chapter 4.4 --- Simulation results --- p.52
Chapter 4.4.1 --- Results of macroscopic quantities --- p.52
Chapter 4.4.2 --- Results of trajectories by simulations --- p.54
Chapter 4.4.3 --- The largest component --- p.55
Chapter 4.4.4 --- Short Summary --- p.56
Chapter 4.5 --- Theoretical analysis --- p.57
Chapter 4.5.1 --- Mean-Field approach --- p.57
Chapter 4.5.2 --- Theoretical results --- p.59
Chapter 4.6 --- Dependence on initial conditions and mean degree --- p.60
Chapter 4.6.1 --- Results for different mean degrees --- p.60
Chapter 4.6.2 --- Results for different initial conditions --- p.61
Chapter 4.7 --- Conclusion --- p.63
Chapter 5 --- Adaptive Rock-Paper-Scissors games --- p.64
Chapter 5.1 --- Introduction --- p.64
Chapter 5.2 --- Adaptive Rock-Paper-Scissors Model --- p.67
Chapter 5.3 --- Simulation results --- p.70
Chapter 5.4 --- Theoretical analysis --- p.73
Chapter 5.4.1 --- Simplifications by threefold-symmetry --- p.73
Chapter 5.4.2 --- Changes in local quantities --- p.74
Chapter 5.4.3 --- Mean-Field approach --- p.75
Chapter 5.4.4 --- Theoretical results --- p.80
Chapter 5.5 --- Dependence on mean degree --- p.82
Chapter 5.6 --- Oriented rewiring method --- p.83
Chapter 5.7 --- Probabilities of winning, drawing and losing --- p.85
Chapter 5.7.1 --- Average probabilities of winning, drawing and losing in the steady state --- p.85
Chapter 5.7.2 --- Degree distribution and the distributions of the probabilities --- p.86
Chapter 5.7.3 --- Brief explanation --- p.88
Chapter 5.7.4 --- Results for a larger μ --- p.89
Chapter 5.7.5 --- Short summary --- p.90
Chapter 5.8 --- Results for general initial conditions --- p.92
Chapter 5.8.1 --- Coupled dynamical equations --- p.92
Chapter 5.8.2 --- Trajectories of the macroscopic quantities --- p.94
Chapter 5.8.3 --- Phases and theoretical ternary phase diagrams --- p.96
Chapter 5.9 --- Conclusion --- p.98
Chapter 6 --- Summary --- p.100
Chapter A --- Coupled dynamical equations for Self-questioning adaptive SG --- p.104
Chapter B --- Theoretical results for Self-questioning adaptive SG with control parameters r and h --- p.106
Chapter C --- Derivations of Mean-Field equations in ARPS model --- p.108
Chapter D --- Derivations of Mean-Field equations for the oriented rewiring method in ARPS model --- p.111
Bibliography --- p.114
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 implications of secondary use of publicly available personal data; and automating technologies and GDPR compliance. The second half of the book shifts focus to novel issues and ideas that drive our digital world. The chapters offer analyses on social and environmental sustainability of smart cities; reconstructing states as information platforms; stakeholder identification using the example of video-based Active and Assisted Living (AAL); and a human-centred approach to dark patterns. This interdisciplinary book takes readers on an intellectual journey into a wide range of issues and cutting-edge ideas to tackle our ever-evolving digital landscape.
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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 management of bipolar disorder. This resource contains 31 chapters on the various management choices available, from both established and novel treatment areas, such as psychoeducation, psychotherapeutic interventions, neuromodulatory approaches, and novel therapeutic targets. The complexity and diversity of the management choices available makes this a continually evolving field and necessitates forward thinking. By discussing both the current management of bipolar disorder and the future developments available, this book provides all clinicians working with patients with bipolar disorder an up-to-date and reflective guide to its management and what the future holds.
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, 62–73. Berlin, Heidelberg: 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, 129–44. Cham: 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, 540–49. Berlin, Heidelberg: 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, 361–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28320-8_31.
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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, 415–43. Cham: 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 & 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 to build on insights gained from a series of exploratory discussions we have engaged in with US-based crowdworkers about how a crowdworker co-operative might work in practice, and begin to sketch out a potential software architecture that could form the basis of future participative approaches to the design and development of a crowdworker co-operative.We begin by describing and reflecting on our own evolving methodology and how it fits with the ‘design justice’ lens we propose for future work. Following this, we present findings from our discussions with crowdworkers about how a crowdwork co-operative might work in practice, including what values workers would like to see embedded in the design. We then finish with the outline of a prototype software architecture for a crowdworker co-operative that could be used as a starting point in future design work in collaboration with crowdworkers.
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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, 173–93. Berlin, Heidelberg: 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, 183–94. Singapore: 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. UDP paradigm is systemically creating new collaborative learning and evolving predictive capacity, which create opportunities to co-manage limited resources. In Thailand since 2015, PA was considered as a means of helping policy makers, researchers, and farmers to deal with increased information, increasingly complex decisions and to professionalize their activities. PA has been widely proposed and developed as providing a basis for improving sustainability of farm-level resource management. However, PA has, up-to-now and in practice, had limited impact in many farm-level resource management contexts, in particular land and labor resources in Thailand. Failures can be attributed to non-delivery, inaccessible technologies and un-coordinated-isolated-silo-short-term-based agencies/actors of government institutions. We have conducted a series of forums on PA for fruit crops with groups of experts and key stakeholders during late 2020 to early 2021. Four plausible future scenarios of PA for fruit crops in Thailand were formulated based on trends and drivers in social, technology, economy, environment, policy and value contexts. It was concluded that policy makers, with long-term political wills, must reinvent the entire institutional system to take advantage of emerging analog and digital technologies. The new system should meet demands and challenges of the BCG model (Bio-Circular-Green economy model). The model called for inclusiveness of actors, i.e., small farms and multi-disciplinary scientists for long-term vision and goal.
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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 decision processes of models. Past attempts at integrating symbolic information with neural networks frequently rely on manually created knowledge bases that are defined in specific configurations, thereby impeding their generalizability to new applications and domains. This chapter seeks to introduce interaction and co-evolving mechanisms between neural models and symbolic knowledge bases. It starts from constructing a panoramic learning framework for learning with all experiences (data, rules, knowledge graphs, etc.). Subsequently, it delves into a novel inversion problem of extracting symbolic knowledge from black-box neural models. Finally, based on the components mentioned above, the chapter will explore a blueprint of a lifelong neural-symbolic system that accommodates human intervention.
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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, 806–21. 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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Ali, Mohammed. "Taxonomy of Industry 4.0 Technologies in Digital Entrepreneurship and Co-Creating Value." In Digital Entrepreneurship and Co-Creating Value Through Digital Encounters, 24–55. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-7416-7.ch002.
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This chapter proposes a taxonomy of Industry 4.0 (I4.0) technologies in the context of digital entrepreneurship and co-creating value. The I4.0 taxonomy ranges from cyber-physical systems and the internet of things (IoT) to big data and cloud computing, among others, which are key enabling technologies (KETs) of I4.0. The chapter examines how these technologies are utilised in digital entrepreneurship and co-creating value, and how they can be integrated to create innovative business models and enhance business operations. The taxonomy offers a comprehensive framework for understanding the different Industry 4.0 technologies and their applications in the rapidly evolving landscape of digital entrepreneurship and co-creating value.
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. Philadelphia, PA: 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. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3534678.3539078.
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Moran, Alejandro, Vincent Canals, Plamen P. Angelov, Christian F. Frasser, Erik S. Skibinsky-Gitlin, Joan Font, Eugeni Isern, Miquel Roca, and Josep L. Rossello. "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. New York, New York, USA: 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. New York, NY, USA: 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.
Full textReports 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. Fort Belvoir, VA: Defense Technical Information Center, October 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, December 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-prepared workforce for the future. The study utilized different interrelated methodological approaches: scoping review and key informant interviews (KIIs), with distinct data-collection methods. The results of the scoping review and KIIs suggest there was no clear common definition of the concept or its dimensions. However, the Philippine articulations of TVCs commonly identify these three categories: (a) critical thinking and other cognitive skills, (b) interpersonal skills, and (c) intrapersonal skills. While these dimensions are prioritized, the data were less clear about the priorities in which TVC concepts and skills should be assessed. To lay the groundwork for potential assessments, the study's recommendation involves formulating a multicomponent assessment of soft skills aligned with basic and higher education curricula. Co-creating a systematic approach to developing assessments of TVCs may include stakeholder consultations, cost-benefit analyses, and meticulous test development phases to ensure technical expertise and appropriateness to local contexts. These assessments may also be leveraged for human resource development and learning in various employment sectors.