Статті в журналах: "Complex systems learning"

1

ELMS, DAVID G. "LEARNING ABOUT COMPLEX SYSTEMS." Civil Engineering Systems 9, no. 3 (October 1992): 265–72. http://dx.doi.org/10.1080/02630259208970653.

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2

Wong, K. Y. Michael, S. Li, and Y. W. Tong. "Complex dynamics in learning systems." Physica A: Statistical Mechanics and its Applications 288, no. 1-4 (December 2000): 397–401. http://dx.doi.org/10.1016/s0378-4371(00)00436-2.

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3

Sterman, John D. "Learning In and About Complex Systems." Reflections: The SoL Journal 1, no. 3 (March 1, 2000): 24–51. http://dx.doi.org/10.1162/152417300570050.

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4

Sterman, John D. "Learning in and about complex systems." System Dynamics Review 10, no. 2-3 (1994): 291–330. http://dx.doi.org/10.1002/sdr.4260100214.

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5

Lawson, Hal A., Dawn Anderson-Butcher, Nancy Petersen, and Carenlee Barkdull. "Design Teams as Learning Systems for Complex Systems Change." Journal of Human Behavior in the Social Environment 7, no. 1-2 (January 2003): 159–79. http://dx.doi.org/10.1300/j137v07n01_11.

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6

Herrington, Jessica, Ted Maddess, Dominique Coy, Corinne Carle, Faran Sabeti, and Marconi Barbosa. "Learning Complex Texture Discrimination." Journal of Vision 18, no. 10 (September 1, 2018): 260. http://dx.doi.org/10.1167/18.10.260.

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7

Kim, Jaehun. "Increasing trust in complex machine learning systems." ACM SIGIR Forum 55, no. 1 (June 2021): 1–3. http://dx.doi.org/10.1145/3476415.3476435.

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Анотація:

Machine learning (ML) has become a core technology for many real-world applications. Modern ML models are applied to unprecedentedly complex and difficult challenges, including very large and subjective problems. For instance, applications towards multimedia understanding have been advanced substantially. Here, it is already prevalent that cultural/artistic objects such as music and videos are analyzed and served to users according to their preference, enabled through ML techniques. One of the most recent breakthroughs in ML is Deep Learning (DL), which has been immensely adopted to tackle such complex problems. DL allows for higher learning capacity, making end-to-end learning possible, which reduces the need for substantial engineering effort, while achieving high effectiveness. At the same time, this also makes DL models more complex than conventional ML models. Reports in several domains indicate that such more complex ML models may have potentially critical hidden problems: various biases embedded in the training data can emerge in the prediction, extremely sensitive models can make unaccountable mistakes. Furthermore, the black-box nature of the DL models hinders the interpretation of the mechanisms behind them. Such unexpected drawbacks result in a significant impact on the trustworthiness of the systems in which the ML models are equipped as the core apparatus. In this thesis, a series of studies investigates aspects of trustworthiness for complex ML applications, namely the reliability and explainability. Specifically, we focus on music as the primary domain of interest, considering its complexity and subjectivity. Due to this nature of music, ML models for music are necessarily complex for achieving meaningful effectiveness. As such, the reliability and explainability of music ML models are crucial in the field. The first main chapter of the thesis investigates the transferability of the neural network in the Music Information Retrieval (MIR) context. Transfer learning, where the pre-trained ML models are used as off-the-shelf modules for the task at hand, has become one of the major ML practices. It is helpful since a substantial amount of the information is already encoded in the pre-trained models, which allows the model to achieve high effectiveness even when the amount of the dataset for the current task is scarce. However, this may not always be true if the "source" task which pre-trained the model shares little commonality with the "target" task at hand. An experiment including multiple "source" tasks and "target" tasks was conducted to examine the conditions which have a positive effect on the transferability. The result of the experiment suggests that the number of source tasks is a major factor of transferability. Simultaneously, it is less evident that there is a single source task that is universally effective on multiple target tasks. Overall, we conclude that considering multiple pre-trained models or pre-training a model employing heterogeneous source tasks can increase the chance for successful transfer learning. The second major work investigates the robustness of the DL models in the transfer learning context. The hypothesis is that the DL models can be susceptible to imperceptible noise on the input. This may drastically shift the analysis of similarity among inputs, which is undesirable for tasks such as information retrieval. Several DL models pre-trained in MIR tasks are examined for a set of plausible perturbations in a real-world setup. Based on a proposed sensitivity measure, the experimental results indicate that all the DL models were substantially vulnerable to perturbations, compared to a traditional feature encoder. They also suggest that the experimental framework can be used to test the pre-trained DL models for measuring robustness. In the final main chapter, the explainability of black-box ML models is discussed. In particular, the chapter focuses on the evaluation of the explanation derived from model-agnostic explanation methods. With black-box ML models having become common practice, model-agnostic explanation methods have been developed to explain a prediction. However, the evaluation of such explanations is still an open problem. The work introduces an evaluation framework that measures the quality of the explanations employing fidelity and complexity. Fidelity refers to the explained mechanism's coherence to the black-box model, while complexity is the length of the explanation. Throughout the thesis, we gave special attention to the experimental design, such that robust conclusions can be reached. Furthermore, we focused on delivering machine learning framework and evaluation frameworks. This is crucial, as we intend that the experimental design and results will be reusable in general ML practice. As it implies, we also aim our findings to be applicable beyond the music applications such as computer vision or natural language processing. Trustworthiness in ML is not a domain-specific problem. Thus, it is vital for both researchers and practitioners from diverse problem spaces to increase awareness of complex ML systems' trustworthiness. We believe the research reported in this thesis provides meaningful stepping stones towards the trustworthiness of ML.

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8

Kempe, Vera, and Patricia J. Brooks. "Second Language Learning of Complex Inflectional Systems." Language Learning 58, no. 4 (December 2008): 703–46. http://dx.doi.org/10.1111/j.1467-9922.2008.00477.x.

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9

Allen, Peter M., and Mark Strathern. "Evolution, Emergence, and Learning in Complex Systems." Emergence 5, no. 4 (December 2003): 8–33. http://dx.doi.org/10.1207/s15327000em0504_4.

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10

Huang, Xueqin, Xianqiang Zhu, Xiang Xu, Qianzhen Zhang, and Ailin Liang. "Parallel Learning of Dynamics in Complex Systems." Systems 10, no. 6 (December 15, 2022): 259. http://dx.doi.org/10.3390/systems10060259.

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Анотація:

Dynamics always exist in complex systems. Graphs (complex networks) are a mathematical form for describing a complex system abstractly. Dynamics can be learned efficiently from the structure and dynamics state of a graph. Learning the dynamics in graphs plays an important role in predicting and controlling complex systems. Most of the methods for learning dynamics in graphs run slowly in large graphs. The complexity of the large graph’s structure and its nonlinear dynamics aggravate this problem. To overcome these difficulties, we propose a general framework with two novel methods in this paper, the Dynamics-METIS (D-METIS) and the Partitioned Graph Neural Dynamics Learner (PGNDL). The general framework combines D-METIS and PGNDL to perform tasks for large graphs. D-METIS is a new algorithm that can partition a large graph into multiple subgraphs. D-METIS innovatively considers the dynamic changes in the graph. PGNDL is a new parallel model that consists of ordinary differential equation systems and graph neural networks (GNNs). It can quickly learn the dynamics of subgraphs in parallel. In this framework, D-METIS provides PGNDL with partitioned subgraphs, and PGNDL can solve the tasks of interpolation and extrapolation prediction. We exhibit the universality and superiority of our framework on four kinds of graphs with three kinds of dynamics through an experiment.

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11

Hall, Geoffrey. "Learning in simple systems." Behavioral and Brain Sciences 32, no. 2 (April 2009): 210–11. http://dx.doi.org/10.1017/s0140525x09000983.

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AbstractStudies of conditioning in simple systems are best interpreted in terms of the formation of excitatory links. The mechanisms responsible for such conditioning contribute to the associative learning effects shown by more complex systems. If a dual-system approach is to be avoided, the best hope lies in developing standard associative theory to deal with phenomena said to show propositional learning.

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12

Tadesse, Aklilu Tilahun, and Pål Ingebrigt Davidsen. "Framework to support personalized learning in complex systems." Journal of Applied Research in Higher Education 12, no. 1 (July 22, 2019): 57–85. http://dx.doi.org/10.1108/jarhe-11-2018-0250.

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Purpose Numerous studies document that students struggle to comprehend complex dynamic systems (CDS). The purpose of this paper is to describe a design framework applied to the creation of a personalized and adaptive online interactive learning environment (OILE) to support students in their study of CDS. Design/methodology/approach A holistic instructional design is applied in five steps to create the OILE. The OILE has the following characteristics: first, it presents a complex, dynamic problem that learners should address in its entirety. It then allows learners to progress through a sequence of learning tasks from easy to complex. Second, after completion of each learning task, the OILE provides learners with supportive information based on their individual performance. The support fades away as learners gain expertise. Third, the OILE tracks and collects information on learners’ progress and generates learning analytics. The OILE was tested on 57 system dynamics students. Findings This paper provides evidence that supports the theoretical design framework from the literature. It also provides a sample from students’ progress logs to demonstrate how the OILE practically facilitated students’ cognitive development. In addition, it provides empirical evidence regarding students’ attitudes toward the OILE that was obtained from administering two questionnaires. Originality/value In light of supportive evidence from the literature, students’ progress in the cognitive domain, and confirmative response in the affective domain, the use of personalized and adaptive OILE to support learning about CDS is considered promising.

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13

Juillé, Hugues, and Jordan B. Pollack. "Coevolutionary Learning and the Design of Complex Systems." Advances in Complex Systems 02, no. 04 (December 1999): 371–93. http://dx.doi.org/10.1142/s0219525999000199.

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Complex systems composed of a large number of loosely coupled entities, with no central coordination offer a number of attractive properties like scalability, robustness or massively distributed computation. However, designing such complex systems presents some challenging issues that are difficult to tackle with traditional top-down engineering methodologies. Coevolutionary learning, which involves the embedding of adaptive learning agents in a fitness environment that dynamically responds to their progress, is proposed as a paradigm to explore a space of complex system designs. It is argued that coevolution offers a flexible framework for the implementation of search heuristics that can efficiently exploit some of the structural properties exhibited by such state spaces. However, several drawbacks have to be overcome in order for coevolutionary learning to achieve continuous progress in the long term. This paper presents some of those problems and introduces a new strategy based on the concept of an "ideal" trainer to address them. This presentation is illustrated with a case study: the discovery of cellular automata rules to implement a classification task. The application of the "ideal" trainer paradigm to that problem resulted in a significant improvement over previously known best rules for this task.

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14

W. Bloom, Jeffrey. "An ecology of mind: teaching – learning complex systems." Kybernetes 42, no. 9/10 (November 11, 2013): 1346–53. http://dx.doi.org/10.1108/k-09-2012-0051.

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15

Weichhart, Georg, and Christian Stary. "Project-based learning for complex adaptive enterprise systems." IFAC-PapersOnLine 50, no. 1 (July 2017): 12991–96. http://dx.doi.org/10.1016/j.ifacol.2017.08.1810.

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16

Dayaram, Kandy. "Dynamics of Workplace Learning: Complicated or Complex Systems?" International Journal of Learning: Annual Review 17, no. 7 (2010): 243–56. http://dx.doi.org/10.18848/1447-9494/cgp/v17i07/47128.

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17

Klopfer, Eric. "The Complex Evolution of Technologies and Pedagogies for Learning about Complex Systems." Systems 9, no. 2 (April 29, 2021): 31. http://dx.doi.org/10.3390/systems9020031.

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18

Schran, Christoph, Fabian L. Thiemann, Patrick Rowe, Erich A. Müller, Ondrej Marsalek, and Angelos Michaelides. "Machine learning potentials for complex aqueous systems made simple." Proceedings of the National Academy of Sciences 118, no. 38 (September 13, 2021): e2110077118. http://dx.doi.org/10.1073/pnas.2110077118.

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Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex systems such as solid–liquid interfaces. Here we present a machine learning framework that enables the efficient development and validation of models for complex aqueous systems. Instead of trying to deliver a globally optimal machine learning potential, we propose to develop models applicable to specific thermodynamic state points in a simple and user-friendly process. After an initial ab initio simulation, a machine learning potential is constructed with minimum human effort through a data-driven active learning protocol. Such models can afterward be applied in exhaustive simulations to provide reliable answers for the scientific question at hand or to systematically explore the thermal performance of ab initio methods. We showcase this methodology on a diverse set of aqueous systems comprising bulk water with different ions in solution, water on a titanium dioxide surface, and water confined in nanotubes and between molybdenum disulfide sheets. Highlighting the accuracy of our approach with respect to the underlying ab initio reference, the resulting models are evaluated in detail with an automated validation protocol that includes structural and dynamical properties and the precision of the force prediction of the models. Finally, we demonstrate the capabilities of our approach for the description of water on the rutile titanium dioxide (110) surface to analyze the structure and mobility of water on this surface. Such machine learning models provide a straightforward and uncomplicated but accurate extension of simulation time and length scales for complex systems.

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19

ALLEN, PETER M. "A COMPLEX SYSTEMS APPROACH TO LEARNING IN ADAPTIVE NETWORKS." International Journal of Innovation Management 05, no. 02 (June 2001): 149–80. http://dx.doi.org/10.1142/s136391960100035x.

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In today's economy, the key ingredients in success and survival are adaptability and the capacity to learn and change. Recent progress in the theory of complex systems provides a new basis for our understanding of how this may actually occur, and the factors on which it depends. Complex systems thinking shows what assumptions underlie the reduction of some part of reality to a mechanical model. They demonstrate that the simplicity and "knowledge" derived from such representations can lead to an understanding that entirely misses the most important, strategic changes that may occur. Complex systems models reveal the key processes that underlie "learning", and recognise the limits to knowledge and the inherent reality of uncertainty. They demonstrate the fundamental importance of internal, microdiversity within systems, as the source of exploration that drives learning. These ideas are explained and presented in a simple model of emergent co-evolution, where the exploration of internal diversity leads to the formation of a complex, with synergetic attributes. The paper describes and models briefly the uncertainties inherent in the definition and development of a new product or service. A further model involving complex products is briefly described which shows the importance of "search" in "knowledge generation" for the success of adaptive industrial networks and clusters. All this leads to the statement of a "law of excess diversity" which states that the long-term survival of a system requires more internal diversity than appears requisite at any time.

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20

Cuevas, Haydee M., Stephen M. Fiore, and Randall L. Oser. "Individual Differences in Complex Task Learning via Interactive Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 17 (July 2000): 171. http://dx.doi.org/10.1177/154193120004401715.

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21

Cheng, Peter C. H. "Electrifying diagrams for learning: principles for complex representational systems." Cognitive Science 26, no. 6 (November 2002): 685–736. http://dx.doi.org/10.1207/s15516709cog2606_1.

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22

Espinosa, A., and T. Porter. "Sustainability, complexity and learning: insights from complex systems approaches." Learning Organization 18, no. 1 (January 11, 2011): 54–72. http://dx.doi.org/10.1108/09696471111096000.

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23

Dougherty, Deborah J., and Danielle D. Dunne. "Enabling Exploratory Learning in Complex, Science-based Innovation Systems." Academy of Management Proceedings 2012, no. 1 (July 2012): 16133. http://dx.doi.org/10.5465/ambpp.2012.16133abstract.

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24

Bouakrif, Farah, Ahmad Taher Azar, Christos K. Volos, Jesus M. Muñoz-Pacheco, and Viet-Thanh Pham. "Iterative Learning and Fractional Order Control for Complex Systems." Complexity 2019 (May 2, 2019): 1–3. http://dx.doi.org/10.1155/2019/7958625.

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25

Jacobson, Michael J., Manu Kapur, Hyo-Jeong So, and June Lee. "The ontologies of complexity and learning about complex systems." Instructional Science 39, no. 5 (September 17, 2010): 763–83. http://dx.doi.org/10.1007/s11251-010-9147-0.

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26

Jovancevic, J., B. Sullivan, and M. Hayhoe. "Learning gaze allocation priorities in complex environments." Journal of Vision 6, no. 6 (March 19, 2010): 480. http://dx.doi.org/10.1167/6.6.480.

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27

Hernández, Adrián, and José M. Amigó. "Attention Mechanisms and Their Applications to Complex Systems." Entropy 23, no. 3 (February 26, 2021): 283. http://dx.doi.org/10.3390/e23030283.

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Deep learning models and graphics processing units have completely transformed the field of machine learning. Recurrent neural networks and long short-term memories have been successfully used to model and predict complex systems. However, these classic models do not perform sequential reasoning, a process that guides a task based on perception and memory. In recent years, attention mechanisms have emerged as a promising solution to these problems. In this review, we describe the key aspects of attention mechanisms and some relevant attention techniques and point out why they are a remarkable advance in machine learning. Then, we illustrate some important applications of these techniques in the modeling of complex systems.

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28

Gabriel, Nicholas, and Neil F. Johnson. "Using Neural Architectures to Model Complex Dynamical Systems." Advances in Artificial Intelligence and Machine Learning 02, no. 02 (2022): 366–84. http://dx.doi.org/10.54364/aaiml.2022.1124.

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The natural, physical and social worlds abound with feedback processes that make the challenge of modeling the underlying system an extremely complex one. This paper proposes an end-to-end deep learning approach to modelling such so-called complex systems which addresses two problems: (1) scientific model discovery when we have only incomplete/partial knowledge of system dynamics; (2) integration of graph-structured data into scientific machine learning (SciML) using graph neural networks. It is well known that deep learning (DL) has had remarkable successin leveraging large amounts of unstructured data into downstream tasks such as clustering, classification, and regression. Recently, the development of graph neural networks has extended DL techniques to graph structured data of complex systems. However, DL methods still appear largely disjointed with established scientific knowledge, and the contribution to basic science is not always apparent. This disconnect has spurred the development of physics-informed deep learning, and more generally, the emerging discipline of SciML. Modelling complex systems in the physical, biological, and social sciences within the SciML framework requires further considerations. We argue the need to consider heterogeneous, graph-structured data as well as the effective scale at which we can observe system dynamics. Our proposal would open up a joint approach to the previously distinct fields of graph representation learning and SciML.

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Silva, Felipe Leno Da, and Anna Helena Reali Costa. "A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems." Journal of Artificial Intelligence Research 64 (March 11, 2019): 645–703. http://dx.doi.org/10.1613/jair.1.11396.

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Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with other agents through autonomous exploration of the environment. However, learning a complex task from scratch is impractical due to the huge sample complexity of RL algorithms. For this reason, reusing knowledge that can come from previous experience or other agents is indispensable to scale up multiagent RL algorithms. This survey provides a unifying view of the literature on knowledge reuse in multiagent RL. We define a taxonomy of solutions for the general knowledge reuse problem, providing a comprehensive discussion of recent progress on knowledge reuse in Multiagent Systems (MAS) and of techniques for knowledge reuse across agents (that may be actuating in a shared environment or not). We aim at encouraging the community to work towards reusing all the knowledge sources available in a MAS. For that, we provide an in-depth discussion of current lines of research and open questions.

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30

Setianingrum, Rannaa. "Kerangka Kerja Berpikir Sistem Menggunakan Ilmu Pengetahuan Alam sebagai Pengetahuan Konten Sistem Kompleks." Biocaster : Jurnal Kajian Biologi 2, no. 4 (October 30, 2022): 215–24. http://dx.doi.org/10.36312/bjkb.v2i4.129.

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Understanding complex systems is an important goal for science learning, so it requires a deep understanding of complex systems knowledge as a subject matter that should be taught in schools. The analysis was conducted to find the dimensions of complex systems and their relevance to the characteristics of systems thinking in the context of science. The systems thinking framework in science learning materials is built based on the dimensions of complex systems and systems thinking. The results of the conceptualization of systems thinking framework are expected to provide a way to select science learning materials as content knowledge of complex systems and contribute to complex systems learning that facilitates systems thinking.

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Chen, Na, and Viktor K. Prasanna. "Learning to Rank Complex Semantic Relationships." International Journal on Semantic Web and Information Systems 8, no. 4 (October 2012): 1–19. http://dx.doi.org/10.4018/jswis.2012100101.

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This paper presents a novel ranking method for complex semantic relationship (semantic association) search based on user preferences. The authors’ method employs a learning-to-rank algorithm to capture each user’s preferences. Using this, it automatically constructs a personalized ranking function for the user. The ranking function is then used to sort the results of each subsequent query by the user. Query results that more closely match the user’s preferences gain higher ranks. Their method is evaluated using a real-world RDF knowledge base created from Freebase linked-open-data. The experimental results show that the authors’ method significantly improves the ranking quality in terms of capturing user preferences, compared with the state-of-the-art.

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Large, David, Petia Sice, Robert Geyer, Geoff O'Brien, and Safwat Mansi. "Complex Processes and Social Systems." International Journal of Systems and Society 2, no. 1 (January 2015): 65–73. http://dx.doi.org/10.4018/ijss.2015010104.

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In this paper the authors consider two contrasting viewpoints; Complex responsive processes which deal with interactions in the present, and complex adaptive systems which focus on learning through the production of what are called mental models. The paper shows that rather than being contradictory, these viewpoints are – at least in some respects - complementary. From the resulting perspective we are able to identify qualitative synergies between the two approaches. Complex responsive processes involve reflections on interactions that take place in time. But you cannot stop time so these present reflections always refer back to a present now gone. Complex adaptive systems are analytic tools. They are not explicitly in the present or in time at all, but they shape our thoughts and actions which are in the present. They shape how people behave, respond and think in a context. In this way people can combine, or reorganise, the approach to complex responsive processes and complex adaptive systems to show how humans address the complex notions of our world.

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Ghili, Soheil, Serima Nazarian, Madjid Tavana, Sepehr Keyvanshokouhi, and Mohammad Taghi Isaai. "A Complex Systems Paradox of Organizational Learning and Knowledge Management." International Journal of Knowledge-Based Organizations 3, no. 3 (July 2013): 53–72. http://dx.doi.org/10.4018/ijkbo.2013070104.

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Many organizations are striving to survive and remain competitive in the current uncertain and rapidly changing economic environment. Businesses must innovate to face this volatility and maintain their competitiveness. Organizational learning is a complex process with many interrelated elements linking knowledge management with organizational innovation. In this paper we use several theories (i.e., organizational learning, knowledge management, organizational innovation, complexity theory, and systems theory) to discover and study the interrelationships among the organizational learning elements. The purpose of this paper is threefold: (1) We identify organizational learning as a mediating variable between knowledge management and organizational innovation; (2) We further present a paradox where decisions that are expected to improve organizational learning, surprisingly do not work; and (3) We show this paradox is not the result of overlooking organizational learning elements, but rather, caused by neglecting to consider the complex interrelationships and interdependencies among them.

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Malik, Hassaan, Muhammad Umar Chaudhry, and Michal Jasinski. "Deep Learning for Molecular Thermodynamics." Energies 15, no. 24 (December 9, 2022): 9344. http://dx.doi.org/10.3390/en15249344.

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Анотація:

The methods used in chemical engineering are strongly reliant on having a solid grasp of the thermodynamic features of complex systems. It is difficult to define the behavior of ions and molecules in complex systems and to make reliable predictions about the thermodynamic features of complex systems across a wide range. Deep learning (DL), which can provide explanations for intricate interactions that are beyond the scope of traditional mathematical functions, would appear to be an effective solution to this problem. In this brief Perspective, we provide an overview of DL and review several of its possible applications within the realm of chemical engineering. DL approaches to anticipate the molecular thermodynamic characteristics of a broad range of systems based on the data that are already available are also described, with numerous cases serving as illustrations.

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35

Shaked, Haim, and Chen Schechter. "Systems Thinking for Principals of Learning- Focused Schools." Journal of School Administration Research and Development 4, no. 1 (July 20, 2019): 18–23. http://dx.doi.org/10.32674/jsard.v4i1.1939.

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Systems thinking involves attempts to understand and improve complex systems, examines systems holistically, and focuses on the way that a system's constituent parts interrelate. This essay provides examples of how systems thinking can enable principals to demonstrate instructional leadership and nurture learning-focused schools in the current era of complexity, diversity, and accountability. These examples illustrate how systems thinking contributes to developing school curriculum, empowering professional learning communities, and fostering performance data interpretation. Overall, systems thinking offers a comprehensive way of both conceptualizing and practicing leadership for learning within the entire school setting, which leads directly to enhancing the quality of instruction and raising students' achievement.

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36

Weng, Tong-Feng, Xin-Xin Cao, and Hui-Jie Yang. "Complex network perspective on modelling chaotic systems via machine learning*." Chinese Physics B 30, no. 6 (June 1, 2021): 060506. http://dx.doi.org/10.1088/1674-1056/abd9b3.

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37

Israfil, Bahram Ismailov. "Deep Learning in Monitoring the Behavior of Complex Technical Systems." Advances in Science, Technology and Engineering Systems Journal 7, no. 5 (September 2022): 10–16. http://dx.doi.org/10.25046/aj070502.

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38

Childs, Christopher M., and Newell R. Washburn. "Embedding domain knowledge for machine learning of complex material systems." MRS Communications 9, no. 3 (July 10, 2019): 806–20. http://dx.doi.org/10.1557/mrc.2019.90.

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39

Qi, Di, and Andrew J. Majda. "Using machine learning to predict extreme events in complex systems." Proceedings of the National Academy of Sciences 117, no. 1 (December 23, 2019): 52–59. http://dx.doi.org/10.1073/pnas.1917285117.

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Extreme events and the related anomalous statistics are ubiquitously observed in many natural systems, and the development of efficient methods to understand and accurately predict such representative features remains a grand challenge. Here, we investigate the skill of deep learning strategies in the prediction of extreme events in complex turbulent dynamical systems. Deep neural networks have been successfully applied to many imaging processing problems involving big data, and have recently shown potential for the study of dynamical systems. We propose to use a densely connected mixed-scale network model to capture the extreme events appearing in a truncated Korteweg–de Vries (tKdV) statistical framework, which creates anomalous skewed distributions consistent with recent laboratory experiments for shallow water waves across an abrupt depth change, where a remarkable statistical phase transition is generated by varying the inverse temperature parameter in the corresponding Gibbs invariant measures. The neural network is trained using data without knowing the explicit model dynamics, and the training data are only drawn from the near-Gaussian regime of the tKdV model solutions without the occurrence of large extreme values. A relative entropy loss function, together with empirical partition functions, is proposed for measuring the accuracy of the network output where the dominant structures in the turbulent field are emphasized. The optimized network is shown to gain uniformly high skill in accurately predicting the solutions in a wide variety of statistical regimes, including highly skewed extreme events. The technique is promising to be further applied to other complicated high-dimensional systems.

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40

Roopaei, Mehdi, Paul Rad, and Mo Jamshidi. "Deep learning control for complex and large scale cloud systems." Intelligent Automation & Soft Computing 23, no. 3 (June 28, 2017): 389–91. http://dx.doi.org/10.1080/10798587.2017.1329245.

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41

McDaniel, Reuben R. "Management Strategies for Complex Adaptive Systems Sensemaking, Learning, and Improvisation." Performance Improvement Quarterly 20, no. 2 (October 22, 2008): 21–41. http://dx.doi.org/10.1111/j.1937-8327.2007.tb00438.x.

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42

Vulpiani, Angelo, and Marco Baldovin. "Effective equations in complex systems: from Langevin to machine learning." Journal of Statistical Mechanics: Theory and Experiment 2020, no. 1 (January 16, 2020): 014003. http://dx.doi.org/10.1088/1742-5468/ab535c.

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43

Sterman, J. D. "Systems dynamics modeling: tools for learning in a complex world." IEEE Engineering Management Review 30, no. 1 (2002): 42. http://dx.doi.org/10.1109/emr.2002.1022404.

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44

Wang, Aiping, Puya Afshar, and Hong Wang. "Complex stochastic systems modelling and control via iterative machine learning." Neurocomputing 71, no. 13-15 (August 2008): 2685–92. http://dx.doi.org/10.1016/j.neucom.2007.06.018.

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45

Wang, Biao, Zhizhong Mao, and Keke Huang. "Detecting outliers for complex nonlinear systems with dynamic ensemble learning." Chaos, Solitons & Fractals 121 (April 2019): 98–107. http://dx.doi.org/10.1016/j.chaos.2019.01.037.

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46

Alvarez, E. "Histaminergic systems of the limbic complex on learning and motivation." Behavioural Brain Research 124, no. 2 (October 15, 2001): 195–202. http://dx.doi.org/10.1016/s0166-4328(01)00213-3.

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47

Kordova, Sigal. "Developing systems thinking in a Project-Based Learning environment." International Journal of Engineering Education 2, no. 1 (June 15, 2020): 63–81. http://dx.doi.org/10.14710/ijee.2.1.63-81.

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As science and engineering projects are becoming increasingly more complex, sophisticated, comprehensive and multidisciplinary, there is a growing need for systems thinking skills to ensure successful project management. Systems thinking plays a major role in the initiation, effective management, and in facilitating inter-organizational tasks. This research assesses the capacity for engineering systems thinking and its contribution in carrying out a multidisciplinary project. The research also reviews the cognitive process through which systems thinking skill is acquired. The study focused on a group of students who have completed their senior design projects in high-tech industry, while their plans were being integrated into existing larger projects in the respective industrial sites. The systems thinking skill of the students was examined according to a questionnaire for assessing the Capacity for Engineering Systems Thinking (CEST). Statistical analysis shows significant differences in the students capacity for systems thinking at the beginning and end of the work (p<0.001). This research demonstrates that systems thinking skills can be improved through awareness and involvement in multidisciplinary projects.

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48

Manzalini, Antonio. "Complex Deep Learning with Quantum Optics." Quantum Reports 1, no. 1 (July 30, 2019): 107–18. http://dx.doi.org/10.3390/quantum1010011.

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The rapid evolution towards future telecommunications infrastructures (e.g., 5G, the fifth generation of mobile networks) and the internet is renewing a strong interest for artificial intelligence (AI) methods, systems, and networks. Processing big data to infer patterns at high speeds and with low power consumption is becoming an increasing central technological challenge. Electronics are facing physically fundamental bottlenecks, whilst nanophotonics technologies are considered promising candidates to overcome the limitations of electronics. Today, there are evidences of an emerging research field, rooted in quantum optics, where the technological trajectories of deep neural networks (DNNs) and nanophotonics are crossing each other. This paper elaborates on these topics and proposes a theoretical architecture for a Complex DNN made from programmable metasurfaces; an example is also provided showing a striking correspondence between the equivariance of convolutional neural networks (CNNs) and the invariance principle of gauge transformations.

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49

Breining, Bonnie L., Nazbanou Nozari, and Brenda Rapp. "Learning in complex, multi-component cognitive systems: Different learning challenges within the same system." Journal of Experimental Psychology: Learning, Memory, and Cognition 45, no. 6 (June 2019): 1093–106. http://dx.doi.org/10.1037/xlm0000630.

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50

Uthamacumaran, A. "A Review of Complex Systems Approaches to Cancer Networks." Complex Systems 29, no. 4 (December 15, 2020): 779–835. http://dx.doi.org/10.25088/complexsystems.29.4.779.

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Cancers remain the leading cause of disease-related pediatric death in North America. The emerging field of complex systems has redefined cancer networks as a computational system. Herein, a tumor and its heterogeneous phenotypes are discussed as dynamical systems having multiple strange attractors. Machine learning, network science and algorithmic information dynamics are discussed as current tools for cancer network reconstruction. Deep learning architectures and computational fluid models are proposed for better forecasting gene expression patterns in cancer ecosystems. Cancer cell decision-making is investigated within the framework of complex systems and complexity theory.

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