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Articoli di riviste sul tema "Data and human knowledge learning"

Autore: Grafiati

Pubblicato: 11 gennaio 2025

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1

Dudyrev, Egor, Ilia Semenkov, Sergei O. Kuznetsov, Gleb Gusev, Andrew Sharp, and Oleg S. Pianykh. "Human knowledge models: Learning applied knowledge from the data." PLOS ONE 17, no. 10 (2022): e0275814. http://dx.doi.org/10.1371/journal.pone.0275814.

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Artificial intelligence and machine learning have demonstrated remarkable results in science and applied work. However, present AI models, developed to be run on computers but used in human-driven applications, create a visible disconnect between AI forms of processing and human ways of discovering and using knowledge. In this work, we introduce a new concept of “Human Knowledge Models” (HKMs), designed to reproduce human computational abilities. Departing from a vast body of cognitive research, we formalized the definition of HKMs into a new form of machine learning. Then, by training the mod

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Weber, Patrick, Nicolas Weber, Michael Goesele, and Rüdiger Kabst. "Prospect for Knowledge in Survey Data." Social Science Computer Review 36, no. 5 (2017): 575–90. http://dx.doi.org/10.1177/0894439317725836.

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Policy making depends on good knowledge of the corresponding target audience. To maximize the designated outcome, it is essential to understand the underlying coherences. Machine learning techniques are capable of analyzing data containing behavioral aspects, evaluations, attitudes, and social values. We show how existing machine learning techniques can be used to identify behavioral aspects of human decision-making and to predict human behavior. These techniques allow to extract high resolution decision functions that enable to draw conclusions on human behavior. Our focus is on voter turnout

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Yao, Quanming. "Towards Human-like Learning from Relational Structured Data." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 20 (2024): 22684. http://dx.doi.org/10.1609/aaai.v38i20.30300.

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Relational structured data is a way of representing knowledge using nodes and edges, while also capturing the meaning of that knowledge in a structured form that can be used for machine learning. Compared with vision and natural language data, relational structured data represents and manipulates structured knowledge, which can be beneficial for tasks that involve reasoning or inference. On the other hand, vision and NLP deal more with unstructured data (like images and text), and they often require different types of models and algorithms to extract useful information or features from the dat

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Kulikovskikh, Ilona, Tomislav Lipic, and Tomislav Šmuc. "From Knowledge Transmission to Knowledge Construction: A Step towards Human-Like Active Learning." Entropy 22, no. 8 (2020): 906. http://dx.doi.org/10.3390/e22080906.

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Machines usually employ a guess-and-check strategy to analyze data: they take the data, make a guess, check the answer, adjust it with regard to the correct one if necessary, and try again on a new data set. An active learning environment guarantees better performance while training on less, but carefully chosen, data which reduces the costs of both annotating and analyzing large data sets. This issue becomes even more critical for deep learning applications. Human-like active learning integrates a variety of strategies and instructional models chosen by a teacher to contribute to learners’ kn

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Anderson, John R. "Methodologies for studying human knowledge." Behavioral and Brain Sciences 10, no. 3 (1987): 467–77. http://dx.doi.org/10.1017/s0140525x00023554.

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AbstractThe appropriate methodology for psychological research depends on whether one is studying mental algorithms or their implementation. Mental algorithms are abstract specifications of the steps taken by procedures that run in the mind. Implementational issues concern the speed and reliability of these procedures. The algorithmic level can be explored only by studying across-task variation. This contrasts with psychology's dominant methodology of looking for within-task generalities, which is appropriate only for studying implementational issues.The implementation-algorithm distinction is

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Kwak, Beong-woo, Youngwook Kim, Yu Jin Kim, Seung-won Hwang, and Jinyoung Yeo. "TrustAL: Trustworthy Active Learning Using Knowledge Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7263–71. http://dx.doi.org/10.1609/aaai.v36i7.20688.

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Active learning can be defined as iterations of data labeling, model training, and data acquisition, until sufficient labels are acquired. A traditional view of data acquisition is that, through iterations, knowledge from human labels and models is implicitly distilled to monotonically increase the accuracy and label consistency. Under this assumption, the most recently trained model is a good surrogate for the current labeled data, from which data acquisition is requested based on uncertainty/diversity. Our contribution is debunking this myth and proposing a new objective for distillation. Fi

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Angrist, Noam, Simeon Djankov, Pinelopi K. Goldberg, and Harry A. Patrinos. "Measuring human capital using global learning data." Nature 592, no. 7854 (2021): 403–8. http://dx.doi.org/10.1038/s41586-021-03323-7.

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AbstractHuman capital—that is, resources associated with the knowledge and skills of individuals—is a critical component of economic development1,2. Learning metrics that are comparable for countries globally are necessary to understand and track the formation of human capital. The increasing use of international achievement tests is an important step in this direction3. However, such tests are administered primarily in developed countries4, limiting our ability to analyse learning patterns in developing countries that may have the most to gain from the formation of human capital. Here we brid

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Kalaycı, Tahir Emre, Bor Bricelj, Marko Lah, Franz Pichler, Matthias K. Scharrer, and Jelena Rubeša-Zrim. "A Knowledge Graph-Based Data Integration Framework Applied to Battery Data Management." Sustainability 13, no. 3 (2021): 1583. http://dx.doi.org/10.3390/su13031583.

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Today, the automotive and transportation sector is undergoing a transformation process to meet the requirements of sustainable and efficient operations. This transformation mainly reveals itself by electric vehicles, hybrid electric vehicles, and electric vehicle sharing. One significant, and the most expensive, component in electric vehicles is the batteries, and the management of batteries is crucial. It is essential to perform constant monitoring of behavior changes for operational purposes and quickly adjust components and operations to these changes. Thus, to address these challenges, we

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Singer-Brodowski, Mandy. "Pedagogical content knowledge of sustainability." International Journal of Sustainability in Higher Education 18, no. 6 (2017): 841–56. http://dx.doi.org/10.1108/ijshe-02-2016-0035.

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Purpose This paper’s purpose is to describe students’ learning processes in a project-based and self-organized seminar on sustainability. A detailed knowledge of typical learning processes is part of a pedagogical content knowledge of sustainability and can therefore contribute to the professional development of university educators. Design/methodology/approach In a project-based and self-organized seminar, a case study has been conducted with the grounded theory’s methodological approach. Data were collected from student interviews, group discussions and observations of students’ planning and

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Abdulkadium, Ahmed Mahdi, Raid Abd Alreda Shekan, and Haitham Ali Hussain. "Application of Data Mining and Knowledge Discovery in Medical Databases." Webology 19, no. 1 (2022): 4912–24. http://dx.doi.org/10.14704/web/v19i1/web19329.

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While technical improvements in the form of computer-based healthcare information applications as well as hardware are enabling collecting of and access to healthcare data wieldier. In this context, there are tools to analyse and examine this medical data once it has been acquired and saved. Analysis of documented medical data records may help in the identification of hidden features and patterns that could significantly increase our understanding of disease onset and treatment therapies. Significantly, the progress in information and communications technologies (ICT) has outpaced our capacity

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Zi, Yuxin, Kaushik Roy, Vignesh Narayanan, and Amit Sheth. "Exploring Alternative Approaches to Language Modeling for Learning from Data and Knowledge." Proceedings of the AAAI Symposium Series 3, no. 1 (2024): 279–86. http://dx.doi.org/10.1609/aaaiss.v3i1.31211.

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Despite their extensive application in language understanding tasks, large language models (LLMs) still encounter challenges including hallucinations - occasional fabrication of information - and alignment issues - lack of associations with human-curated world models (e.g., intuitive physics or common-sense knowledge). Moreover, the black-box nature of LLMs presents significant obstacles in training them effectively to achieve desired behaviors. In particular, modifying the concept embedding spaces of LLMs can be highly intractable. This process involves analyzing the implicit impact of such a

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Singh, Lokendra. "Data Mining: Review, Drifts and Issues." International Journal of Advance Research and Innovation 1, no. 2 (2013): 20–24. http://dx.doi.org/10.51976/ijari.121305.

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This paper gives a good overview of Data and Information or Knowledge has a significant role on human activities. Data mining is the knowledge discovery process by analyzing the large volumes of data from various perspectives and summarizing it into useful information. Due to the importance of extracting knowledge/information from the large data repositories, data mining has become an essential component in various fields of human life. Advancements in Statistics, Machine Learning, Artificial Intelligence, Pattern Recognition and Computation capabilities have evolved the present day’s data min

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Ghadge, Nikhil. "Leveraging Machine Learning to Enhance Information Exploration." Machine Learning and Applications: An International Journal 11, no. 2 (2024): 17–27. http://dx.doi.org/10.5121/mlaij.2024.11203.

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Machine learning algorithms are revolutionizing intelligent search and information discovery capabilities. By incorporating techniques like supervised learning, unsupervised learning, reinforcement learning, and deep learning, systems can automatically extract insights and patterns from vast data repositories. Natural language processing enables deeper comprehension of text, while image recognition unlocks knowledge from visual data. Machine learning powers personalized recommendation engines and accurate sentiment analysis. Integrating knowledge graphs enriches machine learning models with ba

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Hu, Yang, Adriane Chapman, Guihua Wen, and Dame Wendy Hall. "What Can Knowledge Bring to Machine Learning?—A Survey of Low-shot Learning for Structured Data." ACM Transactions on Intelligent Systems and Technology 13, no. 3 (2022): 1–45. http://dx.doi.org/10.1145/3510030.

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Supervised machine learning has several drawbacks that make it difficult to use in many situations. Drawbacks include heavy reliance on massive training data, limited generalizability, and poor expressiveness of high-level semantics. Low-shot Learning attempts to address these drawbacks. Low-shot learning allows the model to obtain good predictive power with very little or no training data, where structured knowledge plays a key role as a high-level semantic representation of human. This article will review the fundamental factors of low-shot learning technologies, with a focus on the operatio

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Jain, Ajay K., and Ana Moreno. "Organizational learning, knowledge management practices and firm’s performance." Learning Organization 22, no. 1 (2015): 14–39. http://dx.doi.org/10.1108/tlo-05-2013-0024.

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Purpose – The study aims at investigating the impact of organizational learning (OL) on the firm’s performance and knowledge management (KM) practices in a heavy engineering organization in India. Design/methodology/approach – The data were collected from 205 middle and senior executives working in the project engineering management division of a heavy engineering public sector organization. The organization manufactures power generation equipment. Questionnaires were administered to collect the data from the respondents. Findings – Results were analyzed using the exploratory factor analysis a

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Burton, Sharon L. "Cybersecurity Leaders: Knowledge Driving Human Capital Development." Scientific Bulletin 26, no. 2 (2021): 109–20. http://dx.doi.org/10.2478/bsaft-2021-0013.

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Abstract Cybersecurity leaders must be able to use critical reading and thinking skills, exercise judgment when policies are not distinct and precise, and have the knowledge, skills, and abilities to tailor technical and planning data to diverse customers’ levels of understanding. Ninety-three percent of cybersecurity leaders do not report directly to the chief operating officer. While status differences influence interactions amid groups, attackers are smarter. With the aim of protecting organizations and reducing risk, knowledge about security must increase. Understanding voids are costly an

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Sabbatini, Federico, and Catia Grimani. "Symbolic knowledge extraction from opaque predictors applied to cosmic-ray data gathered with LISA Pathfinder." Aeronautics and Aerospace Open Access Journal 6, no. 3 (2022): 90–95. http://dx.doi.org/10.15406/aaoaj.2022.06.00145.

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Machine learning models are nowadays ubiquitous in space missions, performing a wide variety of tasks ranging from the prediction of multivariate time series through the detection of specific patterns in the input data. Adopted models are usually deep neural networks or other complex machine learning algorithms providing predictions that are opaque, i.e., human users are not allowed to understand the rationale behind the provided predictions. Several techniques exist in the literature to combine the impressive predictive performance of opaque machine learning models with human-intelligible pre

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Smith, Peter. "Machine Learning Applications in Knowledge Management." European Journal of Information and Knowledge Management 3, no. 2 (2024): 1–13. http://dx.doi.org/10.47941/ejikm.2060.

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Purpose: The general objective of the study was to explore machine learning applications in knowledge management. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This seconda

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Baptist, Kijambu John, D. N. Utami, Bambang Subali, and S. Aloysius. "EFFECTIVENESS OF PROJECT-BASED LEARNING AND 5E LEARNING CYCLE INSTRUCTIONAL MODELS." Jurnal Kependidikan: Penelitian Inovasi Pembelajaran 4, no. 1 (2020): 55–69. http://dx.doi.org/10.21831/jk.v4i1.27107.

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This study was aimed at determining and comparing the effectiveness of project-based learning and 5E learning cycle instructional models in improving the acquisition of new biological knowledge related to the human immune system. This quasi experiment study with a pretest-posttest non-equivalent control group design was conducted in SMAN 1 and SMAN 8 Yogyakarta, Indonesia during the academic year 2018/2019. A cluster sampling technique was used to select 3 eleventh grade classes of natural science from each school. The criterion referenced essay test was used to measure the students’ learning

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He, Tianjia, Tianyuan Yang, and Shin’ichi Konomi. "Human Motion Enhancement and Restoration via Unconstrained Human Structure Learning." Sensors 24, no. 10 (2024): 3123. http://dx.doi.org/10.3390/s24103123.

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Human motion capture technology, which leverages sensors to track the movement trajectories of key skeleton points, has been progressively transitioning from industrial applications to broader civilian applications in recent years. It finds extensive use in fields such as game development, digital human modeling, and sport science. However, the affordability of these sensors often compromises the accuracy of motion data. Low-cost motion capture methods often lead to errors in the captured motion data. We introduce a novel approach for human motion reconstruction and enhancement using spatio-te

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Yang, Yangrui, Sisi Chen, Yaping Zhu, Hao Zhu, and Zhigang Chen. "Knowledge graph empowerment from knowledge learning to graduation requirements achievement." PLOS ONE 18, no. 10 (2023): e0292903. http://dx.doi.org/10.1371/journal.pone.0292903.

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A deep understanding of the relationship between the knowledge acquired and the graduation requirements is essential for students to precisely meet the graduation requirements and to become human resources with specific knowledge, skills and professionalism. In this paper, we define the ontology layer of the knowledge graph by deeply analyzing the relationship between graduation requirement, course and knowledge. Based on the implementation of the concept of Outcome Based Education, we use Knowledge extraction, fusion, reasoning techniques to construct a hierarchical knowledge graph with the m

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Ecarot, Thibaud, Benoît Fraikin, Luc Lavoie, Mark McGilchrist, and Jean-François Ethier. "A Sensitive Data Access Model in Support of Learning Health Systems." Computers 10, no. 3 (2021): 25. http://dx.doi.org/10.3390/computers10030025.

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Given the ever-growing body of knowledge, healthcare improvement hinges more than ever on efficient knowledge transfer to clinicians and patients. Promoted initially by the Institute of Medicine, the Learning Health System (LHS) framework emerged in the early 2000s. It places focus on learning cycles where care delivery is tightly coupled with research activities, which in turn is closely tied to knowledge transfer, ultimately injecting solid improvements into medical practice. Sensitive health data access across multiple organisations is therefore paramount to support LHSs. While the LHS visi

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Widjajanti, Kesi, and Widodo . "PENGEMBANGAN INOVASI ORGANISASI BERBASIS HUMAN CAPITAL, SHARING KNOWLEDGE DAN PEMBELAJARAN ORGANISASIONAL." Jurnal Ekonomi dan Bisnis 15, no. 2 (2014): 86. http://dx.doi.org/10.30659/ekobis.15.2.86-101.

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This study aims to analyze the relationship of organizational learning, knowledgesharing, knowledge sharing and its effect on human capital and organizationalinnovation. Then create a model for organizational innovation-based development ofhuman capital, knowledge sharing and organizational learning SMEs in SemarangRespondents stui entrepreneurs are leaders trained partners with a numberof 150 sampling purposive sampling method. Then to analyze the data inthis study used the Structural Equation Modeling (SEM) with AMOS softwareThe results of this study suggest that innovation-based organizatio

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Ariza-Colpas, Paola Patricia, Enrico Vicario, Ana Isabel Oviedo-Carrascal, et al. "Human Activity Recognition Data Analysis: History, Evolutions, and New Trends." Sensors 22, no. 9 (2022): 3401. http://dx.doi.org/10.3390/s22093401.

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The Assisted Living Environments Research Area–AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative diseases or some disability. This important area is responsible for the development of activity recognition systems—ARS (Activity Recognition Systems), which is a valuable tool when it comes to identifying the type of activity carried out by older adults, to provide them with assist

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Johnson, Paul Andrew. "Actively Pursuing Knowledge In The College Classroom." Journal of College Teaching & Learning (TLC) 8, no. 6 (2011): 17. http://dx.doi.org/10.19030/tlc.v8i6.4279.

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The purpose of this study was to compare course evaluation responses of students enrolled in several sections of a graduate level human growth and development course taught with a traditional lecture/textbook approach to the course evaluation responses of students enrolled subsequent sessions of the same graduate human growth and development course taught with an active learning approach. Quantitative and qualitative data indicated that students in the sections taught with an active learning approach rated those sections significantly higher than students in sections taught with a traditional

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Breunig, Karl Joachim. "Limitless learning: assessing social media use for global workplace learning." Learning Organization 23, no. 4 (2016): 249–70. http://dx.doi.org/10.1108/tlo-07-2014-0041.

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Purpose This empirical paper aims to assess how social media can foster workplace learning within a globally dispersed project environment. In general, there are few studies on the use of social media in organizations, and many of these emphasize on issues related to knowledge transfer. Although learning traditionally has been as acquisition of knowledge, increasingly researchers point to learning-as-participation occurring through work collaboration. Social media promise increased opportunities for communication and collaboration, extending the context of collaboration beyond the local settin

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Babovic, Vladan. "Introducing knowledge into learning based on genetic programming." Journal of Hydroinformatics 11, no. 3-4 (2009): 181–93. http://dx.doi.org/10.2166/hydro.2009.041.

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This work examines various methods for creating empirical equations on the basis of data while taking advantage of knowledge about the problem domain. It is demonstrated that the use of high level concepts aid in evolving equations that are easier to interpret by domain specialists. The application of the approach to real-world problems reveals that the utilization of such concepts results in equations with performance equal or superior to that of human experts. Finally, it is argued that the algorithm is best used as a hypothesis generator assisting scientists in the discovery process.

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Xiao, Yan. "Intelligent Manufacturing Engineers’ Knowledge Transfer and Innovation Capability: From the Perspective of Big Data Acceptance Attitude." Proceedings of Business and Economic Studies 7, no. 4 (2024): 32–38. http://dx.doi.org/10.26689/pbes.v7i4.8056.

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In the face of intelligent manufacturing (or smart manufacturing) human resource shortage, the training of industrial engineers in the field of intelligent manufacturing is of great significance. In academia, the positive link between learning transfer and knowledge innovation is recognized by most scholars, while the learner’s attitude toward big data decision-making, as a cognitive perception, affects learning transfer from the learner’s experienced engineering paradigm to the intelligent manufacturing paradigm. Thus, learning transfer can be regarded as a result of the learner’s attitude, a

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V. Rafael, Felicisima. "Predicting Job Change among Data Scientists using Machine Learning Technique." 14th GCBSS Proceeding 2022 14, no. 2 (2022): 1. http://dx.doi.org/10.35609/gcbssproceeding.2022.2(77).

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In the knowledge and data-driven economy, countless ramifications were attributed to great contribution of data scientists in transforming business and industries by using various data science tools in recognizing and generating patterns in data points to generate insights. The study aimed at applying data science in human resources, and generates actionable intelligence, and HR analytics to better understand employees' perception towards the company, work environment. The researcher used the processes of Knowledge Discovery in Databases (KDD) method. Knowledge discovery in databases is the no

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Jones, Matt, Tyler R. Scott, and Michael C. Mozer. "Human-like Learning in Temporally Structured Environments." Proceedings of the AAAI Symposium Series 3, no. 1 (2024): 553. http://dx.doi.org/10.1609/aaaiss.v3i1.31273.

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Natural environments have correlations at a wide range of timescales. Human cognition is tuned to this temporal structure, as seen by power laws of learning and memory, and by spacing effects whereby the intervals between repeated training data affect how long knowledge is retained. Machine learning is instead dominated by batch iid training or else relatively simple nonstationarity assumptions such as random walks or discrete task sequences. The main contributions of our work are: (1) We develop a Bayesian model formalizing the brain's inductive bias for temporal structure and show our model

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Cui, Lvye, and Haoran Yu. "Data-Driven Knowledge-Aware Inference of Private Information in Continuous Double Auctions." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 9 (2024): 10012–20. http://dx.doi.org/10.1609/aaai.v38i9.28864.

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Inferring the private information of humans from their strategic behavioral data is crucial and challenging. The main approach is first obtaining human behavior functions (which map public information and human private information to behavior), enabling subsequent inference of private information from observed behavior. Most existing studies rely on strong equilibrium assumptions to obtain behavior functions. Our work focuses on continuous double auctions, where multiple traders with heterogeneous rationalities and beliefs dynamically trade commodities and deriving equilibria is generally intr

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Hamed, Ameera Fares, Abdulsatar Shaker Salman, Muzaffer Yassen, Dhafer Asim Aldabagh, and Volodymyr Savenko. "Human Capital Development in Knowledge Economies." Journal of Ecohumanism 3, no. 5 (2024): 798–816. http://dx.doi.org/10.62754/joe.v3i5.3938.

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Background: As global economies shift towards knowledge-based models, the importance of human capital in promoting sustainable development is becoming more apparent. This article explores the complex relationship between human capital development and the growth of knowledge-based economies, emphasising the crucial role of education, skill improvement, and ongoing learning programmes. Objective: To analyse the key factors that enhance human capital development in knowledge-based economies. The articles aim to find successful techniques for developing a workforce that can drive innovation and ec

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Engström, Annika, and Nikolas Käkelä. "Early steps in learning about organizational learning in customization settings." Learning Organization 26, no. 1 (2019): 27–43. http://dx.doi.org/10.1108/tlo-09-2018-0150.

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Purpose This study aims to empirically investigate the role of learning for suppliers of individualized customizations from a communication perspective. Design/methodology/approach Five companies providing individualized customizations are investigated through an in-depth qualitative approach. The empirical material is based on data from five presentations in one workshop and seven interviews. Findings Four important categories of communication processes between suppliers and customers that stimulate learning were identified: the identification and confirmation of existing knowledge, the ident

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Filippova, Anna, Connor Gilroy, Ridhi Kashyap, et al. "Humans in the Loop: Incorporating Expert and Crowd-Sourced Knowledge for Predictions Using Survey Data." Socius: Sociological Research for a Dynamic World 5 (January 2019): 237802311882015. http://dx.doi.org/10.1177/2378023118820157.

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Survey data sets are often wider than they are long. This high ratio of variables to observations raises concerns about overfitting during prediction, making informed variable selection important. Recent applications in computer science have sought to incorporate human knowledge into machine-learning methods to address these problems. The authors implement such a “human-in-the-loop” approach in the Fragile Families Challenge. The authors use surveys to elicit knowledge from experts and laypeople about the importance of different variables to different outcomes. This strategy offers the option

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Rahman, Md Asifur, Lew Sook Ling, and Ooi Shih Yin. "Interactive Learning System for Learning Calculus." F1000Research 11 (March 11, 2024): 307. http://dx.doi.org/10.12688/f1000research.73595.2.

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Background IT tools has brought a new perspective to collaborative learning where students do not just sit in a chair and swallow lecture content but instead participate in creating and sharing knowledge. However, calculus learning augmented reality application has limitation in promoting a human collaboration in learning. Purpose This research develops an interactive application for learning calculus that promotes human-system interaction via augmented reality (AR) and human-human interaction through chat functions. The study examines the effect of both interactivities on learning experience

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Tuggle, Francis D. "Gaps and progress in our knowledge of learning organizations." Learning Organization 23, no. 6 (2016): 444–57. http://dx.doi.org/10.1108/tlo-09-2016-0059.

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Purpose This study aims to review previously published issues of The Learning Organization (TLO) to assess what progress has been made since the journal started in terms of what is known about learning organizations. The author also aims to identify important gaps in what is still to be discovered about organizations that learn, partly to single out promising areas to be investigated. Design/methodology/approach The author reviews all the previously published articles in the first 20 volumes printed and reviews each issue in each volume. The author classifies the methodology undertaken by each

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Cooper, Andrew L., Joseph R. Huscroft, Robert E. Overstreet, and Benjamin T. Hazen. "Knowledge management for logistics service providers: the role of learning culture." Industrial Management & Data Systems 116, no. 3 (2016): 584–602. http://dx.doi.org/10.1108/imds-06-2015-0262.

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Purpose – Knowledge management capabilities have proven to be key success factors for organizations within our increasingly information-based economy. Although knowledge management literature has a rich history, less is known about how an organization’s learning culture affects outcomes realized via knowledge management initiatives. Moreover, there is a dearth of understanding regarding how to successfully operationalize knowledge management activities in order to achieve performance in the dynamic logistics and supply chain management environment. Rooted in competence-based theory, the purpos

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Tortorella, Guilherme Luz, Samanta Viana, and Diego Fettermann. "Learning cycles and focus groups." Learning Organization 22, no. 4 (2015): 229–40. http://dx.doi.org/10.1108/tlo-02-2015-0008.

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Purpose – This study aims to propose a complementary method to the A3 information collection, data analysis and capturing and sharing knowledge to facilitate problem solving in a general framework. The incorporation of this method minimizes the difficulties identified in the literature focused on continuous improvement of processes. The method comprises combining triangulation techniques utilizing focus groups for a survey of qualitative data and the approach called Look – Ask – Model – Discuss – Act (LAMDA), which was originally designed for cycles of knowledge creation applied in product dev

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Gupta, Kishor Datta, Sunzida Siddique, Roy George, Marufa Kamal, Rakib Hossain Rifat, and Mohd Ariful Haque. "Physics Guided Neural Networks with Knowledge Graph." Digital 4, no. 4 (2024): 846–65. http://dx.doi.org/10.3390/digital4040042.

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Over the past few decades, machine learning (ML) has demonstrated significant advancements in all areas of human existence. Machine learning and deep learning models rely heavily on data. Typically, basic machine learning (ML) and deep learning (DL) models receive input data and its matching output. Within the model, these models generate rules. In a physics-guided model, input and output rules are provided to optimize the model’s learning, hence enhancing the model’s loss optimization. The concept of the physics-guided neural network (PGNN) is becoming increasingly popular among researchers a

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Anam, Mamoona, Harikrishnan D., Prithiviraj A., Regin R., Kalyan Chakravarthi M., and Praghash K. "Automation of Internet of things Using Deep Learning on the Basis of Extraction of Data." Webology 19, no. 1 (2022): 1398–412. http://dx.doi.org/10.14704/web/v19i1/web19093.

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Sharing and reusing data across apps and industries is critical for the Internet of Things to realize its full potential. However, various IoT systems exist, each with its networks, depictions, and interaction swatches. To address this problem, On the one extreme, the Fed4IoT project has established an IoT virtualization software that merges data from various sources. It makes the data that users need accessible in their choice operating system, but on the other hand. Data is converted into an impartial, standard transfer protocol to make this possible. The preferred format is the next generat

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Memmi, Daniel. "Data coding takes place within a context." Behavioral and Brain Sciences 20, no. 1 (1997): 77–78. http://dx.doi.org/10.1017/s0140525x97360026.

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Recoding the data for relational learning is both easier and more difficult than it might appear. Human beings routinely find the appropriate representation for a given problem because coding always takes place within the framework of a domain, theory, or background knowledge. How this can be achieved is still highly speculative, but should probably be investigated with hybrid models.

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Nam Bach, Thanh, Denise Junger, Cristóbal Curio, and Oliver Burgert. "Towards Human Action Recognition during Surgeries using De-identified Video Data." Current Directions in Biomedical Engineering 8, no. 1 (2022): 109–12. http://dx.doi.org/10.1515/cdbme-2022-0028.

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Abstract With the progress of technology in modern hospitals, an intelligent perioperative situation recognition will gain more relevance due to its potential to substantially improve surgical workflows by providing situation knowledge in real-time. Such knowledge can be extracted from image data by machine learning techniques but poses a privacy threat to the staff’s and patients’ personal data. De-identification is a possible solution for removing visual sensitive information. In this work, we developed a YOLO v3 based prototype to detect sensitive areas in the image in real-time. These are

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Gad, Gad, and Zubair Fadlullah. "Federated Learning via Augmented Knowledge Distillation for Heterogenous Deep Human Activity Recognition Systems." Sensors 23, no. 1 (2022): 6. http://dx.doi.org/10.3390/s23010006.

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Deep learning-based Human Activity Recognition (HAR) systems received a lot of interest for health monitoring and activity tracking on wearable devices. The availability of large and representative datasets is often a requirement for training accurate deep learning models. To keep private data on users’ devices while utilizing them to train deep learning models on huge datasets, Federated Learning (FL) was introduced as an inherently private distributed training paradigm. However, standard FL (FedAvg) lacks the capability to train heterogeneous model architectures. In this paper, we propose Fe

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Rahman, Md Asifur, Lew Sook Ling, and Ooi Shih Yin. "Interactive Learning System for Learning Calculus." F1000Research 11 (March 14, 2022): 307. http://dx.doi.org/10.12688/f1000research.73595.1.

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Background: IT tools assist in creating a more participative and independent learning environment. They have brought a new perspective to collaborative learning where students do not just sit in a chair and swallow lecture content but instead participate in creating and sharing knowledge. However, interactivity promoted through the implementation of technology is limited in many cases. Purpose: This research develops an interactive application for learning calculus that promotes human-system interaction via augmented reality (AR) and human-human interaction through chat functions. The study ex

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Liu, Ning, Achintha Ihalage, Hangfeng Zhang, Henry Giddens, Haixue Yan, and Yang Hao. "Interactive human–machine learning framework for modelling of ferroelectric–dielectric composites." Journal of Materials Chemistry C 8, no. 30 (2020): 10352–61. http://dx.doi.org/10.1039/c9tc06073a.

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Xiong, Ding, and Lu Yan. "A Classification Learning Research based on Discriminative Knowledge-Leverage Transfer." International Journal of Ambient Computing and Intelligence 9, no. 4 (2018): 52–68. http://dx.doi.org/10.4018/ijaci.2018100104.

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Abstract (sommario):

Current transfer learning models study the source data for future target inferences within a major view, the whole source data should be used to explore the shared knowledge structure. However, human resources are constrained, the source domain data is collected as a whole in the real scene. However, this is not realistic, this data is associated with the target domain. A generalized empirical risk minimization model (GERM) is proposed in this article with discriminative knowledge-leverage (KL). The empirical risk minimization (ERM) principle is extended to the transfer learning setting. The t

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Zhang, Weizhi, and Jonathan Yong Chung Ee. "An Intelligent Knowledge Graph-Based Directional Data Clustering and Feature Selection for Improved Education." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6s (2023): 22–33. http://dx.doi.org/10.17762/ijritcc.v11i6s.6807.

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With advancements in technology and the increasing availability of data, there is a growing interest in leveraging intelligent learning models to enhance the educational experience and improve learning outcomes. The construction of intelligent learning models, supported by knowledge graphs, has emerged as a promising approach to revolutionizing the field of education. With the vast number of educational resources and data available, knowledge graphs provide a structured and interconnected representation of knowledge, enabling intelligent systems to leverage this wealth of information. This pap

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Cano, A., A. R. Masegosa, and S. Moral. "A Method for Integrating Expert Knowledge When Learning Bayesian Networks From Data." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 41, no. 5 (2011): 1382–94. http://dx.doi.org/10.1109/tsmcb.2011.2148197.

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Pan, Wensheng, Timin Gao, Yan Zhang, et al. "Semi-Supervised Blind Image Quality Assessment through Knowledge Distillation and Incremental Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 5 (2024): 4388–96. http://dx.doi.org/10.1609/aaai.v38i5.28236.

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Blind Image Quality Assessment (BIQA) aims to simulate human assessment of image quality. It has a great demand for labeled data, which is often insufficient in practice. Some researchers employ unsupervised methods to address this issue, which is challenging to emulate the human subjective system. To this end, we introduce a unified framework that combines semi-supervised and incremental learning to address the mentioned issue. Specifically, when training data is limited, semi-supervised learning is necessary to infer extensive unlabeled data. To facilitate semi-supervised learning, we use kn

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P., Arunprasad. "Guiding metaphors for knowledge-intensive firms." International Journal of Organizational Analysis 24, no. 4 (2016): 743–72. http://dx.doi.org/10.1108/ijoa-07-2015-0887.

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Abstract (sommario):

Purpose The purpose of this paper is to conceptualize and empirically test the impact of strategic human resource management (HRM) practices on learning outcomes and also to examine whether this relationship is contingent on knowledge strategy in a sample of knowledge-intensive firms like software companies in India. Design/methodology/approach Data were collected through a questionnaire, and the software companies were chosen based on the listing in the NASSCOM annual report. A total of 32 companies participated in this research study, and the survey was conducted in two phases. Findings The

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