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Journal articles on the topic 'Data and human knowledge learning'
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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 (October 20, 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 models with human processing capabilities, we learned human-like knowledge, that humans can not only understand, but also compute, modify, and apply. We used several datasets from different applied fields to demonstrate the advantages of HKMs, including their high predictive power and resistance to noise and overfitting. Our results proved that HKMs can efficiently mine knowledge directly from the data and can compete with complex AI models in explaining the main data patterns. As a result, our study reveals the great potential of HKMs, particularly in the decision-making applications where “black box” models cannot be accepted. Moreover, this improves our understanding of how well human decision-making, modeled by HKMs, can approach the ideal solutions in real-life problems.
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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 (September 12, 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, for which we use data acquired by the European Social Survey on the German national vote. We show how to train an artificial expert and how to extract the behavioral aspects to build optimized policies. Our method achieves an increase in adjusted R2 of 102% compared to a classic logistic regression prediction. We further evaluate the performance of our method compared to other machine learning techniques such as support vector machines and random forests. The results show that it is possible to better understand unknown variable relationships.
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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 (March 24, 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 data. Human-like Learning develops methods that can harness relational structures and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. With Human-like Learning, the learning algorithm is efficient and can adapt to new or unseen situations, which is crucial in real-world applications where environments may change unpredictably. Moreover, the models are easier for humans to understand and interpret, which is important for transparency and trust in AI systems. In this talk, we present our recent attempts towards human-like learning from relational structured data.
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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 (August 18, 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’ knowledge, while machine active learning strategies lack versatile tools for shifting the focus of instruction away from knowledge transmission to learners’ knowledge construction. We approach this gap by considering an active learning environment in an educational setting. We propose a new strategy that measures the information capacity of data using the information function from the four-parameter logistic item response theory (4PL IRT). We compared the proposed strategy with the most common active learning strategies—Least Confidence and Entropy Sampling. The results of computational experiments showed that the Information Capacity strategy shares similar behavior but provides a more flexible framework for building transparent knowledge models in deep learning.
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Anderson, John R. "Methodologies for studying human knowledge." Behavioral and Brain Sciences 10, no. 3 (September 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 related to a number of other “levels” considered in cognitive science. Its realization in Anderson's ACT theory of cognition is discussed. Research at the algorithmic level is more promising because it is hard to make further fundamental scientific progress at the implementational level with the methodologies available. Protocol data, which are appropriate only for algorithm-level theories, provide a richer source than data at the implementational level. Research at the algorithmic level will also yield more insight into fundamental properties of human knowledge because it is the level at which significant learning transitions are defined.The best way to study the algorithmic level is to look for differential learning outcomes in pedagogical experiments that manipulate instructional experience. This provides control and prediction in realistically complex learning situations. The intelligent tutoring paradigm provides a particularly fruitful way to implement such experiments.The implications of this analysis for the issue of modularity of mind, the status of language, research on human/computer interaction, and connectionist models are also examined.
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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 (June 28, 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. First, we found example forgetting, which indicates the loss of knowledge learned across iterations. Second, for this reason, the last model is no longer the best teacher-- For mitigating such forgotten knowledge, we select one of its predecessor models as a teacher, by our proposed notion of "consistency". We show that this novel distillation is distinctive in the following three aspects; First, consistency ensures to avoid forgetting labels. Second, consistency improves both uncertainty/diversity of labeled data. Lastly, consistency redeems defective labels produced by human annotators.
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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 (March 10, 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 bridge this gap by constructing a globally comparable database of 164 countries from 2000 to 2017. The data represent 98% of the global population and developing economies comprise two-thirds of the included countries. Using this dataset, we show that global progress in learning—a priority Sustainable Development Goal—has been limited, despite increasing enrolment in primary and secondary education. Using an accounting exercise that includes a direct measure of schooling quality, we estimate that the role of human capital in explaining income differences across countries ranges from a fifth to half; this result has an intermediate position in the wide range of estimates provided in earlier papers in the literature5–13. Moreover, we show that average estimates mask considerable heterogeneity associated with income grouping across countries and regions. This heterogeneity highlights the importance of including countries at various stages of economic development when analysing the role of human capital in economic development. Finally, we show that our database provides a measure of human capital that is more closely associated with economic growth than current measures that are included in the Penn world tables version 9.014 and the human development index of the United Nations15.
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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 (February 2, 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 propose a knowledge graph-based data integration framework for simplifying access and analysis of data accumulated through the operations of vehicles and related transportation systems. The proposed framework aims to enable the effortless analysis and navigation of integrated knowledge and the creation of additional data sets from this knowledge to use during the application of data analysis and machine learning. The knowledge graph serves as a significant component to simplify the extraction, enrichment, exploration, and generation of data in this framework. We have developed it according to the human-centered design, and various roles of the data science and machine learning life cycle can use it. Its main objective is to streamline the exploration and interaction with the integrated data to maximize human productivity. Finally, we present a battery use case to show the feasibility and benefits of the proposed framework. The use case illustrates the usage of the framework to extract knowledge from raw data, navigate and enrich it with additional knowledge, and generate data sets.
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Singer-Brodowski, Mandy. "Pedagogical content knowledge of sustainability." International Journal of Sustainability in Higher Education 18, no. 6 (September 4, 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 organization meetings. Findings The results of the case study show that students’ learning processes vary depending on their pre-seminar sustainability experiences. Two types have been established: sustainability newcomers and sustainability experts. Furthermore, the results indicate the importance of emotions in the involvement with sustainability. Research limitations/implications The significance of the case study is limited by a small number of cases. Also, the results are specific for a seminar self-organized by the students and can therefore not simply be transferred to other seminars. Practical implications Knowledge of specific learning processes and a possible conceptual change in sustainability classes could be an important issue in the professional development of university educators because it would increase the educators’ pedagogical content knowledge. Originality/value The triangulation of qualitative data mainly served the investigation of students’ perspectives and therefore the understanding of subjective preferences, experiences and learning processes in the field of higher education for sustainable development (HESD).
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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 (January 20, 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 to assess summarise, and extract insight from the data. Today, database management system has equipped us with the fundamental tools for the effective storage as well as lookup of massive data sets, but the topic of how to allow human beings to interpret and analyse huge data remains a challenging and unsolved challenge. So, sophisticated methods for automated data mining and knowledge discovery are required to deal with large data. In this study, an effort was made employing machine learning approach to acquire knowledge that will aid various personnel in taking decisions that will guarantee that the sustainability objectives on Health is achieved. Finally, the present data mining methodologies with data mining methods and also its deployment tools that are more helpful for healthcare services are addressed in depth.
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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 (May 20, 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 adjustments on the myriad parameters within LLMs and the resulting inductive biases. We propose a novel architecture that wraps powerful function approximation architectures within an outer, interpretable read-out layer. This read-out layer can be scrutinized to explicitly observe the effects of concept modeling during the training of the LLM. Our method stands in contrast with gradient-based implicit mechanisms, which depend solely on adjustments to the LLM parameters and thus evade scrutiny. By conducting extensive experiments across both generative and discriminative language modeling tasks, we evaluate the capabilities of our proposed architecture relative to state-of-the-art LLMs of similar sizes. Additionally, we offer a qualitative examination of the interpretable read-out layer and visualize the concepts it captures. The results demonstrate the potential of our approach for effectively controlling LLM hallucinations and enhancing the alignment with human expectations.
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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 mining applications and these applications have enriched the various fields of human life including business, education, medical, scientific etc. Hence, this paper discusses the various improvements in the field of data mining from past to the present and explores the future trends
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Ghadge, Nikhil. "Leveraging Machine Learning to Enhance Information Exploration." Machine Learning and Applications: An International Journal 11, no. 2 (June 28, 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 background knowledge for enhanced accuracy and explainability. Applications span voice search, anomaly detection, predictive analytics, text mining, and data clustering. However, interpretable AI models are crucial for enabling transparency and trustworthiness. Key challenges include limited training data, complex domain knowledge requirements, and ethical considerations around bias and privacy. Ongoing research that combines machine learning, knowledge representation, and human-centered design will advance intelligent search and discovery. The collaboration between artificial and human intelligence holds the potential to revolutionize information access and knowledge acquisition.
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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 (June 30, 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 operation of structured knowledge under different low-shot conditions. We also introduce other techniques relevant to low-shot learning. Finally, we point out the limitations of low-shot learning, the prospects and gaps of industrial applications, and future research directions.
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Jain, Ajay K., and Ana Moreno. "Organizational learning, knowledge management practices and firm’s performance." Learning Organization 22, no. 1 (January 12, 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 and multiple regression analysis techniques. The findings showed that all the factors of OL, i.e. collaboration and team working, performance management, autonomy and freedom, reward and recognition and achievement orientation were found to be the positive predictors of different dimensions of firm’s performance and KM practices. Research limitations/implications – The implications are discussed to improve the OL culture to enhance the KM practices so that firm’s performance could be sustained financially or otherwise. The study is conducted in one division of a large public organization, hence generalizability is limited. Originality/value – This is an original study carried out in a large a heavy engineering organization in India that validates the theory of OL and KM in the Indian context.
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Burton, Sharon L. "Cybersecurity Leaders: Knowledge Driving Human Capital Development." Scientific Bulletin 26, no. 2 (December 1, 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 and increased breach chances are imminent. Burning questions exist. What are needed technological learnings for cybersecurity leaders to become smarter and remain ahead of attackers? How might these technologies hasten the understanding of the ‘what,’ ‘how,’ and ‘why’ reasons and key drivers for organizational behaviors. This article offers comparative analyses for cybersecurity leaders to engage in the questioning of practices, scrutinize entrenched assumptions about technology, customary practices, and query technology’s outputs by pursuing to comprehend all assumptions that could influence operations. Because understanding continues to rely upon progressively multifaceted epistemic technologies, outcomes of the research suggest that the salience of status distinctions is of central significance to the development of ongoing and proactive technological learning and up scaling solutions.
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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 (July 26, 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 prediction explanations, as for instance the application of symbolic knowledge extraction procedures. In this paper are reported the results of different knowledge extractors applied to an ensemble predictor capable of reproducing cosmic-ray data gathered on board the LISA Pathfinder space mission. A discussion about the readability/fidelity trade-off of the extracted knowledge is also presented.
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Smith, Peter. "Machine Learning Applications in Knowledge Management." European Journal of Information and Knowledge Management 3, no. 2 (July 12, 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 secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to machine learning applications in knowledge management. Preliminary empirical review revealed that integrating machine learning (ML) into knowledge management (KM) systems significantly enhanced decision-making processes, knowledge sharing, and collaboration within organizations. ML-powered tools improved efficiency and accuracy by automating tasks and providing predictive insights, leading to better organizational performance and innovation. However, the study also highlighted the challenges of data quality, integration, and user adaptation, emphasizing the need for comprehensive strategies and investments to maximize ML benefits in KM. Ultimately, the study underscored ML's transformative potential in creating a more efficient, innovative, and competitive organizational environment. Unique Contribution to Theory, Practice and Policy: The Knowledge-Based View (KBV) of the Firm, Technology Acceptance Model (TAM) and Socio-Technical Systems Theory may be used to anchor future studies on machine learning applications in knowledge management. The study recommended integrating dynamic ML capabilities into theoretical frameworks, emphasizing the interplay between ML algorithms and human cognition. It advised organizations to invest in robust ML infrastructure, foster a culture of continuous learning, and adopt user-centric design principles. Policymakers were urged to establish ethical standards and incentivize best practices in data governance. Practical recommendations included automating routine tasks to enhance efficiency, using ML to foster collaborative innovation, and adopting continuous improvement and adaptation mindsets to keep ML applications relevant and effective.
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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 (May 5, 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 achievements and the data collected were analyzed using SPSS version 23. The results show that both PjBL and 5E learning instructional models were effective to improve the students’ ability to acquire new biological knowledge related to the human immune system. PjBL model was more effective than 5E learning cycle model in improving the students’ ability to acquire new biological knowledge related to the human immune system.
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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 (May 14, 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-temporal attention-based graph convolutional networks (ST-ATGCNs), which efficiently learn the human skeleton structure and the motion logic without requiring prior human kinematic knowledge. This method enables unsupervised motion data restoration and significantly reduces the costs associated with obtaining precise motion capture data. Our experiments, conducted on two extensive motion datasets and with real motion capture sensors such as the SONY mocopi, demonstrate the method’s effectiveness in enhancing the quality of low-precision motion capture data. The experiments indicate the ST-ATGCN’s potential to improve both the accessibility and accuracy of motion capture technology.
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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 (October 12, 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 main line of "knowledge-course-graduation requirements. In the process of knowledge extraction, in order to alleviate the huge labor overhead brought by traditional extraction methods, this paper adopts a transfer learning method to extract triadic knowledge using the multi-task framework EERJE, Finally, knowledge reasoning was also performed with the help of LLM to further expand the knowledge scope. The comprehensiveness, correctness and relatedness of the data were evaluated through the experiment, and the F1 value of the ternary group extraction was 87.76%, the accuracy rate of entity classification was 85.42%, the data coverage was more comprehensive, and the results showed that the data quality was better, and the knowledge graph constructed in this way can fully optimize the organization and management of teaching resources, help students intuitively and comprehensively grasp the correlation and difference between graduation requirements and various knowledge points, and let the Students can carry out personalized independent learning through the navigation mode of knowledge graph, strengthen their weak links, and complete the relevant graduation requirements, which effectively improves the degree of students’ graduation requirements achievement. This new paradigm of knowledge graph enabled teaching is of reference significance for engineering education majors to improve the degree of graduation requirements achievement.
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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 (February 26, 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 vision is well established, security requirements to support them are not. Health data exchange approaches have been implemented (e.g., HL7 FHIR) or proposed (e.g., blockchain-based methods), but none cover the entire LHS requirement spectrum. To address this, the Sensitive Data Access Model (SDAM) is proposed. Using a representation of agents and processes of data access systems, specific security requirements are presented and the SDAM layer architecture is described, with an emphasis on its mix-network dynamic topology approach. A clinical application benefiting from the model is subsequently presented and an analysis evaluates the security properties and vulnerability mitigation strategies offered by a protocol suite following SDAM and in parallel, by FHIR.
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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 (July 1, 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 organizations to develop human capital, knowledge sharing and organizational learning, organizational innovation first built with human capital that is influenced by increasing organizational learning. The second innovation is built with a knowledge sharing organization. The third innovation is built with human capital organization.
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Ariza-Colpas, Paola Patricia, Enrico Vicario, Ana Isabel Oviedo-Carrascal, Shariq Butt Aziz, Marlon Alberto Piñeres-Melo, Alejandra Quintero-Linero, and Fulvio Patara. "Human Activity Recognition Data Analysis: History, Evolutions, and New Trends." Sensors 22, no. 9 (April 29, 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 assistance. that allows you to carry out your daily activities with complete normality. This article aims to show the review of the literature and the evolution of the different techniques for processing this type of data from supervised, unsupervised, ensembled learning, deep learning, reinforcement learning, transfer learning, and metaheuristics approach applied to this sector of science. health, showing the metrics of recent experiments for researchers in this area of knowledge. As a result of this article, it can be identified that models based on reinforcement or transfer learning constitute a good line of work for the processing and analysis of human recognition activities.
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Johnson, Paul Andrew. "Actively Pursuing Knowledge In The College Classroom." Journal of College Teaching & Learning (TLC) 8, no. 6 (May 18, 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 lecture/textbook approach.
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Breunig, Karl Joachim. "Limitless learning: assessing social media use for global workplace learning." Learning Organization 23, no. 4 (May 9, 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 setting. However, there exists limited research on how social media can foster workplace learning, for example, between globally dispersed colleagues. Design/methodology/approach The study is based on an exploratory, in-depth single case study of an international professional service firm’s implementation of an internal wiki system to address the research question: how are social media utilized in an organization to foster workplace learning among its dispersed individual experts? Data are gathered in 35 semi-structured interviews, as well as documents studies and observations. Data are coded and analyzed utilizing the context and learning factors of workplace learning. Findings The paper shows how the wiki system enables hybrid knowledge management strategies linked to virtual collaboration on daily project tasks, involving documentation, search, interaction and knowledge exchange, as well as socialization and learning from practice among dispersed groups and individuals. The learning mechanisms involved in virtual collaboration do not differ much from what is reported on face-to-face workplace learning, however, the context factors are extended beyond the local setting. Practical implications The findings identify four determinants for using the wiki that can be of use to other organizations implementing similar virtual collaboration technology. First, the wiki must directly relate to the daily work by offering interactive and updated information concerning current project challenges. Second, the system must enable transparency in the daily project work to allow search. Third, the intention with the search is of lesser degree to identify encyclopedic information than it is to visualize individual competence. Fourth, the quality assurance of the data posted at the wiki is important. Originality/value The study reveals how an international knowledge-based organization can utilize social media to leverage knowledge and experiences from multiple geographically dispersed projects by enabling virtual collaboration. Extant empirical research on workplace learning emphasizes on face-to-face interactions in groups, for example, when engineers, or accountants, in teams interact and collaborate at client premises. However, there exists limited knowledge concerning how workplace learning can be achieved through virtual collaboration.
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Babovic, Vladan. "Introducing knowledge into learning based on genetic programming." Journal of Hydroinformatics 11, no. 3-4 (July 1, 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 (August 26, 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, and it becomes the intermediary state between their attitude and knowledge innovation. This paper reviews prior research on knowledge transfer and develops hypotheses on the relationships between learner acceptance attitude, knowledge transfer, and knowledge innovation.
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V. Rafael, Felicisima. "Predicting Job Change among Data Scientists using Machine Learning Technique." 14th GCBSS Proceeding 2022 14, no. 2 (December 28, 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 nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns or relationships within a dataset (10,000 examples, 0 special attributes, and 14 regular attributes) to make important decisions. RapidMiner was used perform the KDD processes of selecting, pre-processing, data transformation, data mining using machine learning algorithm. Accordingly, Decision Tree was found to be the learning algorithm fit for the ExampleSet. Further, among 14 attributes, the most important attribute to split on was the city_development_index. This implies that the best predictor variable for job change among data scientists was the city_development_index. Consequently, the prediction model has 92.1% confidence that a Male who works in a city with a development index of 0.920, with relevant data science experience, not presently enrolled in the university, high school graduate, with 5 years of work experience, presently working in a Funded Start-up company with 50-99 employees, works for the first time with training hours=24 was predicted will "Not Change" a job. The model has 77.78% accuracy, and 81.70% precision. Keywords: Data Scientist, Data Science, Job Change, Human Resource Analytics
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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 (May 20, 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 accounts for key features of human learning and memory. (2) We translate the model into a new gradient-based optimization technique for neural networks that endows them with human-like temporal inductive bias and improves their performance in realistic nonstationary tasks. Our technical approach is founded on Bayesian inference over 1/f noise, a statistical signature of many natural environments with long-range, power law correlations. We derive a new closed-form solution to this problem by treating the state of the environment as a sum of processes on different timescales and applying an extended Kalman filter to learn all timescales jointly. We then derive a variational approximation of this model for training neural networks, which can be used as a drop-in replacement for standard optimizers in arbitrary architectures. Our optimizer decomposes each weight in the network as a sum of subweights with different learning and decay rates and tracks their joint uncertainty. Thus knowledge becomes distributed across timescales, enabling rapid adaptation to task changes while retaining long-term knowledge and avoiding catastrophic interference. Simulations show improved performance in environments with realistic multiscale nonstationarity. Finally, we present simulations showing our model gives essentially parameter-free fits of learning, forgetting, and spacing effects in human data. We then explore the analogue of human spacing effects in a deep net trained in a structured environment where tasks recur at different rates and compare the model's behavioral properties to those of people.
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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 (March 24, 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 intractable. We develop a knowledge-aware machine learning-based framework to infer each trader's private cost vectors for producing different units of its commodity. Our key idea is to learn behavior functions by incorporating the statistical knowledge about private costs given the observed trader asking behavior across the population. Specifically, we first use a neural network to characterize each trader's behavior function. Second, we leverage the statistical knowledge to derive the posterior distribution of each trader's private costs given its observed asks. Third, through designing a novel loss function, we utilize the knowledge-based posterior distributions to guide the learning of the neural network. We conduct extensive experiments on a large experimental dataset, and demonstrate the superior performance of our framework over baselines in inferring the private information of humans.
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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 (September 4, 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 economic growth via a thorough investigation. Methodology: This study combines empirical analysis with a thorough evaluation of current literature using a mixed-methods methodology. Data were collected diligently from many sources, such as education data, workforce development programmes, and case studies from successful knowledge-based economies to provide a comprehensive overview of the present situation. Results: The results highlight the significant impact of human capital on enhancing innovation, productivity, and competitive advantage in knowledge-based economies. It highlights the need to invest in education and skill development to ensure long-term economic strength. The report emphasises that certain investments are crucial to continue making progress towards sustainable development. Conclusion: The article suggests that successful human capital development is crucial for success in the knowledge-based economy. Policymakers, educators, and business leaders are urged to develop thorough policies that equip people with the necessary information and skills to succeed in a constantly changing economic landscape. This article argues for a comprehensive strategy for developing human capital as the foundation for creating a strong and successful knowledge-driven society.
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Engström, Annika, and Nikolas Käkelä. "Early steps in learning about organizational learning in customization settings." Learning Organization 26, no. 1 (January 14, 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 identification of knowledge gaps and the creation of new knowledge, the definition of relations and procedures and evaluation and learning. Practical implications These findings can help suppliers of individualized customizations become aware of the important role of organizational learning in their day-to-day operations and the value of improving as a learning organization. Originality/value This cross-disciplinary study brings together organizational learning and customization research. It is a study that focuses on communication in customization tasks as a base for learning.
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Filippova, Anna, Connor Gilroy, Ridhi Kashyap, Antje Kirchner, Allison C. Morgan, Kivan Polimis, Adaner Usmani, and Tong Wang. "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 to subset the data before prediction or to incorporate human knowledge as scores in prediction models, or both together. The authors find that human intervention is not obviously helpful. Human-informed subsetting reduces predictive performance, and considered alone, approaches incorporating scores perform marginally worse than approaches that do not. However, incorporating human knowledge may still improve predictive performance, and future research should consider new ways of doing so.
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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 and how that learning experience affects the performance of learning. Methods The research adopted a quasi-experimental study design and pre-post test data analysis to evaluate the effect of interactivities on learning experience and consequently the effect of learning experience on learning performance. The subjects were exposed to the developed application for learning the calculus chapter “Solid of Revolution” in a controlled environment. The study validated its research framework through partial least squares path modelling and tested three hypotheses via pre-and post-test evaluation. Conclusions The results found that both interactivities affect learning experience positively; human-human interactivity has a higher impact than the human-system interactivity. It was also found that learning performance as part of the learning experience increased from pre-test to post-test.
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Tuggle, Francis D. "Gaps and progress in our knowledge of learning organizations." Learning Organization 23, no. 6 (September 12, 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 published article as being one of the following: a conceptual study, a case study or the analysis of other data. Keywords are assessed to get insights into the shifts in research themes pursued over the years. Findings There has been a substantial increase in the number of published papers over time. The number and percentage of articles that are conceptual in nature has declined somewhat over the years. The number and percentage of articles that involve case studies has increased over the years. The number and percentage of articles that involve analyzing data has increased significantly over the years. There has been a significant shift in research focus away from topics such as management and organizational development to topics such as knowledge management and social networks. Three major areas of gaps in our knowledge of learning organizations are identified: what it means to be a learning organization, how effective are learning organizations and what contextual factors influence learning organizations. Research limitations/implications Although other journals occasionally publish research on learning organizations, attention in this paper is solely focused upon TLO. Practical implications Addressing some of the research questions identified should provide insights that will assist practicing managers. Originality/value Although not a meta analysis of this journal’s research, the author presents a “thematic analysis” of research published in this journal, and the results and insights should prove interesting and useful to scholars in the field seeking rich areas to study.
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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 (April 11, 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 purpose of this paper is to examine the role that learning culture plays with regard to knowledge management capabilities, human capital, and organizational performance at logistics service providers. Design/methodology/approach – This study uses survey data from 448 managers and covariance based structural equation modeling to assess how knowledge management, learning culture, and human capital influence organizational performance. Findings – The results of this study indicate that knowledge management has a significant positive relationship with learning culture and human capital. There was also an indirect effect of knowledge management through learning culture on human capital and organizational performance. Interestingly, human capital did not have a significant relationship with organizational performance as hypothesized. Practical implications – The results support the vital role that leaders and managers have in creating a culture that is conducive to the success of knowledge management initiatives. Originality/value – This study goes beyond the simple direct relationship between knowledge management and personal and organizational outcomes that is usually examined by testing learning culture as an important mediator.
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Tortorella, Guilherme Luz, Samanta Viana, and Diego Fettermann. "Learning cycles and focus groups." Learning Organization 22, no. 4 (May 11, 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 development processes. Design/methodology/approach – The methodology proposed in this work follows the A3 report approach including focus groups’ techniques in the planning step and problem analysis and the use of the LAMDA learning cycle, aiming to fill the gaps in A3 method. Therefore, the methodology includes five macro steps, which are divided into smaller steps. Note that the inclusion of the LAMDA learning cycle in the A3 report is called A3LAMDA. In addition, macro steps from 1 to 4 belong to “knowledge creation” step and macro step 5 deals with the “Capture of knowledge” step. Along the proposed methodology application, greater focus will be given to techniques incorporated in the A3 report, as they represent the main contribution of this method. Findings – The proposed approach to the A3 report was more concise and comprehensive, allowing different views and perspectives to be considered to understand the problem and find solutions through the focus groups method. In addition, the development of a structured questionnaire for the interviews encouraged the participants to present their opinions regarding the problem hypotheses. The use of the LAMDA learning cycle was essential to capture and share the knowledge acquired during the problem-solving process. It allowed not only the reflection on the aspects that have either worked or not but also the understanding of how to do the next work differently. Originality/value – 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 the general framework. The incorporation of this method minimizes the difficulties identified in the literature that focused on continuous improvement of processes.
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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 (October 10, 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 and industry professionals. It has been applied in numerous fields such as healthcare, medicine, environmental science, and control systems. This review was conducted using four specific research questions. We obtained papers from six different sources and reviewed a total of 81 papers, based on the selected keywords. In addition, we have specifically addressed the difficulties and potential advantages of the PGNN. Our intention is for this review to provide guidance for aspiring researchers seeking to obtain a deeper understanding of the PGNN.
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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 (January 20, 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 generation service interfaces, and it is now being standardized by the European Telecommunications Standardization Institute Industrial Standards Group on Frame of reference Data Governance. The elements particular basis of interpretation data to next-generation service interfaces, passed over to the particular platform and transformed to the destination format, are known as Something Shields. Hand-building thing visors are possible, but it requires time and work, partially due to the variety of low-level data many sensors provide. As a result, it's necessary to aid the human developer and, ideally, completely automate the gathering, enriching, and exporting data to NGSI-LD. Automation has many potential answers, but it frequently necessitates a huge amount of manually tagged datasets, which is impractical in numerous internet of things scenarios. Knowledge infusion identifies with an application method that uses expert knowledge to match a schema or ontology obtained from information supplied a discourse marker or ontology, setting the framework for identifying gathering as much information and support transformation.
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Memmi, Daniel. "Data coding takes place within a context." Behavioral and Brain Sciences 20, no. 1 (March 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 (July 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 then deidentified using common image obfuscation techniques. Our approach shows that it is principle suitable for de-identifying sensitive data in OR images and contributes to a privacyrespectful way of processing in the context of situation recognition in the OR.
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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 (December 20, 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 Federated Learning via Augmented Knowledge Distillation (FedAKD) for distributed training of heterogeneous models. FedAKD is evaluated on two HAR datasets: A waist-mounted tabular HAR dataset and a wrist-mounted time-series HAR dataset. FedAKD is more flexible than standard federated learning (FedAvg) as it enables collaborative heterogeneous deep learning models with various learning capacities. In the considered FL experiments, the communication overhead under FedAKD is 200X less compared with FL methods that communicate models’ gradients/weights. Relative to other model-agnostic FL methods, results show that FedAKD boosts performance gains of clients by up to 20 percent. Furthermore, FedAKD is shown to be relatively more robust under statistical heterogeneous scenarios.
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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 examines the effect of both interactivities on learning experience and how that learning experience affects the performance of learning. Methods: The research adopted a quasi-experimental study design and pre-post test data analysis to evaluate the effect of interactivities on learning experience and consequently the effect of learning experience on learning performance. The subjects were exposed to the developed application for learning the calculus chapter ‘Revolution of Solids” in a controlled environment. The study validated its research framework through partial least squares path modelling and tested three hypotheses via pre-and post-test evaluation. Conclusions: The results found that both interactivities affect learning experience positively; human-human interactivity has a higher impact than human-system interactivity. It was also found that learning performance as part of the learning experience increased from pre-test to post-test.
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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 (October 2018): 52–68. http://dx.doi.org/10.4018/ijaci.2018100104.
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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 theoretical upper bound of generalized ERM (GERM) is given for the practical discriminative transfer learning. The subset of the source domain data can be automatically selected in the model, and the source domain data is associated with the target domain. It can solve with only some knowledge of the source domain being available, thus it can avoid the negative transfer effect which is caused by the whole source domain dataset in the real scene. Simulation results show that the proposed algorithm is better than the traditional transfer learning algorithm in simulation data sets and real data sets.
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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 (June 6, 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 paper aimed to construct an effective automated Intelligent Learning Model with the integration of Knowledge Graphs. The automated intelligent model comprises the directional data clustering (DDC) integrated with the Voting based Integrated effective Feature Selection model through the LSTM-integrated Grasshopper Algorithm (LSTM_GOA). The data for analysis is collected from educational institutions in China. Through the framed LSTM_GOA model the performance is evaluated fro the analysis of the student educational performance. The simulation analysis expressed that the developed model exhibits a higher classification performance compared with the conventional technique in terms of accuracy and Mean Square Error (MSE).
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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 (October 2011): 1382–94. http://dx.doi.org/10.1109/tsmcb.2011.2148197.
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Pan, Wensheng, Timin Gao, Yan Zhang, Xiawu Zheng, Yunhang Shen, Ke Li, Runze Hu, Yutao Liu, and Pingyang Dai. "Semi-Supervised Blind Image Quality Assessment through Knowledge Distillation and Incremental Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 5 (March 24, 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 knowledge distillation to assign pseudo-labels to unlabeled data, preserving analytical capability. To gradually improve the quality of pseudo labels, we introduce incremental learning. However, incremental learning can lead to catastrophic forgetting. We employ Experience Replay by selecting representative samples during multiple rounds of semi-supervised learning, to alleviate forgetting and ensure model stability. Experimental results show that the proposed approach achieves state-of-the-art performance across various benchmark datasets. After being trained on the LIVE dataset, our method can be directly transferred to the CSIQ dataset. Compared with other methods, it significantly outperforms unsupervised methods on the CSIQ dataset with a marginal performance drop (-0.002) on the LIVE dataset. In conclusion, our proposed method demonstrates its potential to tackle the challenges in real-world production processes.
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P., Arunprasad. "Guiding metaphors for knowledge-intensive firms." International Journal of Organizational Analysis 24, no. 4 (September 5, 2016): 743–72. http://dx.doi.org/10.1108/ijoa-07-2015-0887.
Full textAbstract:
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 universalistic approach revealed that organizational learning outcomes can be enhanced by focusing on specific individual HRM practices. Also, the fit between HRM practices and knowledge strategy revealed that the interaction effect between individual and knowledge strategy have had an increased impact on the learning outcomes. Practical implications HRM practices can be aligned to the targeted knowledge strategy of the organization and maximize specific organizational learning outcome to achieve sustained competitive advantage. Knowledge-intensive firms can measure their knowledge strategy and gauge whether it is complemented with HRM practices for better tangible and intangible outcomes. Originality/value The proposed model can benefit the firms to analyse the extent of contribution of HRM practices towards the organizational learning process. It also helps to understand how an organization can be productive by focusing on specific learning outcomes and establishing a tighter link between the select individual HRM practices and the defined knowledge strategy.