Relevant bibliographies by topics / Data and human knowledge learning

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Data and human knowledge learning'

Author: Grafiati
Published: 11 January 2025

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Journal articles on the topic "Data and human knowledge learning"

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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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Dissertations / Theses on the topic "Data and human knowledge learning"

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McKay, Elspeth, and elspeth@rmit edu au. "Instructional strategies integrating cognitive style construct: A meta-knowledge processing model." Deakin University. School of Computing and Mathematics, 2000. http://tux.lib.deakin.edu.au./adt-VDU/public/adt-VDU20061011.122556.

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The overarching goal of this dissertation was to evaluate the contextual components of instructional strategies for the acquisition of complex programming concepts. A meta-knowledge processing model is proposed, on the basis of the research findings, thereby facilitating the selection of media treatment for electronic courseware. When implemented, this model extends the work of Smith (1998), as a front-end methodology, for his glass-box interpreter called Bradman, for teaching novice programmers. Technology now provides the means to produce individualized instructional packages with relative ease. Multimedia and Web courseware development accentuate a highly graphical (or visual) approach to instructional formats. Typically, little consideration is given to the effectiveness of screen-based visual stimuli, and curiously, students are expected to be visually literate, despite the complexity of human-computer interaction. Visual literacy is much harder for some people to acquire than for others! (see Chapter Four: Conditions-of-the-Learner) An innovative research programme was devised to investigate the interactive effect of instructional strategies, enhanced with text-plus-textual metaphors or text-plus-graphical metaphors, and cognitive style, on the acquisition of a special category of abstract (process) programming concept. This type of concept was chosen to focus on the role of analogic knowledge involved in computer programming. The results are discussed within the context of the internal/external exchange process, drawing on Ritchey's (1980) concepts of within-item and between-item encoding elaborations. The methodology developed for the doctoral project integrates earlier research knowledge in a novel, interdisciplinary, conceptual framework, including: from instructional science in the USA, for the concept learning models; British cognitive psychology and human memory research, for defining the cognitive style construct; and Australian educational research, to provide the measurement tools for instructional outcomes. The experimental design consisted of a screening test to determine cognitive style, a pretest to determine prior domain knowledge in abstract programming knowledge elements, the instruction period, and a post-test to measure improved performance. This research design provides a three-level discovery process to articulate: 1) the fusion of strategic knowledge required by the novice learner for dealing with contexts within instructional strategies 2) acquisition of knowledge using measurable instructional outcome and learner characteristics 3) knowledge of the innate environmental factors which influence the instructional outcomes This research has successfully identified the interactive effect of instructional strategy, within an individual's cognitive style construct, in their acquisition of complex programming concepts. However, the significance of the three-level discovery process lies in the scope of the methodology to inform the design of a meta-knowledge processing model for instructional science. Firstly, the British cognitive style testing procedure, is a low cost, user friendly, computer application that effectively measures an individual's position on the two cognitive style continua (Riding & Cheema,1991). Secondly, the QUEST Interactive Test Analysis System (Izard,1995), allows for a probabilistic determination of an individual's knowledge level, relative to other participants, and relative to test-item difficulties. Test-items can be related to skill levels, and consequently, can be used by instructional scientists to measure knowledge acquisition. Finally, an Effect Size Analysis (Cohen,1977) allows for a direct comparison between treatment groups, giving a statistical measurement of how large an effect the independent variables have on the dependent outcomes. Combined with QUEST's hierarchical positioning of participants, this tool can assist in identifying preferred learning conditions for the evaluation of treatment groups. By combining these three assessment analysis tools into instructional research, a computerized learning shell, customised for individuals' cognitive constructs can be created (McKay & Garner,1999). While this approach has widespread application, individual researchers/trainers would nonetheless, need to validate with an extensive pilot study programme (McKay,1999a; McKay,1999b), the interactive effects within their specific learning domain. Furthermore, the instructional material does not need to be limited to a textual/graphical comparison, but could be applied to any two or more instructional treatments of any kind. For instance: a structured versus exploratory strategy. The possibilities and combinations are believed to be endless, provided the focus is maintained on linking of the front-end identification of cognitive style with an improved performance outcome. My in-depth analysis provides a better understanding of the interactive effects of the cognitive style construct and instructional format on the acquisition of abstract concepts, involving spatial relations and logical reasoning. In providing the basis for a meta-knowledge processing model, this research is expected to be of interest to educators, cognitive psychologists, communications engineers and computer scientists specialising in computer-human interactions.
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Pomponio, Laura. "Definition of a human-machine learning process from timed observations : application to the modelling of human behaviourfor the detection of abnormal behaviour of old people at home." Thesis, Aix-Marseille, 2012. http://www.theses.fr/2012AIXM4358.

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L'acquisition et la modélisation de connaissances ont été abordés jusqu'à présent selon deux approches principales : les êtres humains (experts) à l'aide des méthodologies de l'Ingénierie des Connaissances et le Knowledge Management, et les données à l'aide des techniques relevant de la découverte de connaissances à partir du contenu de bases de données (fouille de données). Cette thèse porte sur la conception d'un processus d'apprentissage conjoint par l'être humain et la machine combinant une approche de modélisation des connaissances de type Ingénierie des Connaissances (TOM4D, Timed Observation Modelling for Diagnosis) et une approche d'apprentissage automatique fondée sur un processus de découverte de connaissances à partir de données datées (TOM4L, Timed Observation Mining for Learning). Ces deux approches étant fondées sur la Théorie des Observations Datées, les modèles produits sont représentés dans le même formalisme ce qui permet leur comparaison et leur combinaison. Le mémoire propose également une méthode d'abstraction, inspiée des travaux de Newell sur le "Knowledge Level'' et fondée sur le paradigme d'observation datée, qui a pour but de traiter le problème de la différence de niveau d'abstraction inhérent entre le discours d'un expert et les données mesurées sur un système par un processus d'abstractions successives. Les travaux présentés dans ce mémoire ayant été menés en collaboration avec le CSTB de Sophia Antipolis (Centre Scientifique et Technique du Bâtiment), ils sont appliqués à la modélisation de l'activité humaine dans le cadre de l'aide aux personnes âgées maintenues à domicile
Knowledge acquisition has been traditionally approached from a primarily people-driven perspective, through Knowledge Engineering and Management, or from a primarily data-driven approach, through Knowledge Discovery in Databases, rather than from an integral standpoint. This thesis proposes then a human-machine learning approach that combines a Knowledge Engineering modelling approach called TOM4D (Timed Observation Modelling For Diagnosis) with a process of Knowledge Discovery in Databases based on an automatic data mining technique called TOM4L (Timed Observation Mining For Learning). The combination and comparison between models obtained through TOM4D and those ones obtained through TOM4L is possible, owing to that TOM4D and TOM4L are based on the Theory of Timed Observations and share the same representation formalism. Consequently, a learning process nourished with experts' knowledge and knowledge discovered in data is defined in the present work. In addition, this dissertation puts forward a theoretical framework of abstraction levels, in line with the mentioned theory and inspired by the Newell's Knowledge Level work, in order to reduce the broad gap of semantic content that exists between data, relative to an observed process, in a database and what can be inferred in a higher level; that is, in the experts' discursive level. Thus, the human-machine learning approach along with the notion of abstraction levels are then applied to the modelling of human behaviour in smart environments. In particular, the modelling of elderly people's behaviour at home in the GerHome Project of the CSTB (Centre Scientifique et Technique du Bâtiment) of Sophia Antipolis, France
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Gaspar, Paulo Miguel da Silva. "Computational methods for gene characterization and genomic knowledge extraction." Doctoral thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/13949.

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Doutoramento conjunto MAPi em Ciências da Computação
Motivation: Medicine and health sciences are changing from the classical symptom-based to a more personalized and genetics-based paradigm, with an invaluable impact in health-care. While advancements in genetics were already contributing significantly to the knowledge of the human organism, the breakthrough achieved by several recent initiatives provided a comprehensive characterization of the human genetic differences, paving the way for a new era of medical diagnosis and personalized medicine. Data generated from these and posterior experiments are now becoming available, but its volume is now well over the humanly feasible to explore. It is then the responsibility of computer scientists to create the means for extracting the information and knowledge contained in that data. Within the available data, genetic structures contain significant amounts of encoded information that has been uncovered in the past decades. Finding, reading and interpreting that information are necessary steps for building computational models of genetic entities, organisms and diseases; a goal that in due course leads to human benefits. Aims: Numerous patterns can be found within the human variome and exome. Exploring these patterns enables the computational analysis and manipulation of digital genomic data, but requires specialized algorithmic approaches. In this work we sought to create and explore efficient methodologies to computationally calculate and combine known biological patterns for various purposes, such as the in silico optimization of genetic structures, analysis of human genes, and prediction of pathogenicity from human genetic variants. Results: We devised several computational strategies to evaluate genes, explore genomes, manipulate sequences, and analyze patients’ variomes. By resorting to combinatorial and optimization techniques we were able to create and combine sequence redesign algorithms to control genetic structures; by combining the access to several web-services and external resources we created tools to explore and analyze available genetic data and patient data; and by using machine learning we developed a workflow for analyzing human mutations and predicting their pathogenicity.
Motivação: A medicina e as ciências da saúde estão atualmente num processo de alteração que muda o paradigma clássico baseado em sintomas para um personalizado e baseado na genética. O valor do impacto desta mudança nos cuidados da saúde é inestimável. Não obstante as contribuições dos avanços na genética para o conhecimento do organismo humano até agora, as descobertas realizadas recentemente por algumas iniciativas forneceram uma caracterização detalhada das diferenças genéticas humanas, abrindo o caminho a uma nova era de diagnóstico médico e medicina personalizada. Os dados gerados por estas e outras iniciativas estão disponíveis mas o seu volume está muito para lá do humanamente explorável, e é portanto da responsabilidade dos cientistas informáticos criar os meios para extrair a informação e conhecimento contidos nesses dados. Dentro dos dados disponíveis estão estruturas genéticas que contêm uma quantidade significativa de informação codificada que tem vindo a ser descoberta nas últimas décadas. Encontrar, ler e interpretar essa informação são passos necessários para construir modelos computacionais de entidades genéticas, organismos e doenças; uma meta que, em devido tempo, leva a benefícios humanos. Objetivos: É possível encontrar vários padrões no varioma e exoma humano. Explorar estes padrões permite a análise e manipulação computacional de dados genéticos digitais, mas requer algoritmos especializados. Neste trabalho procurámos criar e explorar metodologias eficientes para o cálculo e combinação de padrões biológicos conhecidos, com a intenção de realizar otimizações in silico de estruturas genéticas, análises de genes humanos, e previsão da patogenicidade a partir de diferenças genéticas humanas. Resultados: Concebemos várias estratégias computacionais para avaliar genes, explorar genomas, manipular sequências, e analisar o varioma de pacientes. Recorrendo a técnicas combinatórias e de otimização criámos e conjugámos algoritmos de redesenho de sequências para controlar estruturas genéticas; através da combinação do acesso a vários web-services e recursos externos criámos ferramentas para explorar e analisar dados genéticos, incluindo dados de pacientes; e através da aprendizagem automática desenvolvemos um procedimento para analisar mutações humanas e prever a sua patogenicidade.
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Zeni, Mattia. "Bridging Sensor Data Streams and Human Knowledge." Doctoral thesis, University of Trento, 2017. http://eprints-phd.biblio.unitn.it/2724/1/Thesis.pdf.

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Generating useful knowledge out of personal big data in form of sensor streams is a difficult task that presents multiple challenges due to the intrinsic characteristics of these type of data, namely their volume, velocity, variety and noisiness. This problem is a well-known long standing problem in computer science called the Semantic Gap Problem. It was originally defined in the research area of image processing as "... the lack of coincidence between the information that one can extract from the visual data and the interpretation that the same data have for a user in a given situation..." [Smeulders et al., 2000]. In the context of this work, the lack of coincidence is between low-level raw streaming sensor data collected by sensors in a machine-readable format and higher-level semantic knowledge that can be generated from these data and that only humans can understand thanks to their intelligence, habits and routines. This thesis addresses the semantic gap problem in the context above, proposing an interdisciplinary approach able to generate human level knowledge from streaming sensor data in open domains. It leverages on two different research fields: one regarding the collection, management and analysis of big data and the field of semantic computing, focused on ontologies, which respectively map to the two elements of the semantic gap mentioned above. The contributions of this thesis are: • The definition of a methodology based on the idea that the user and the world surrounding him can be modeled, defining most of the elements of her context as entities (locations, people, objects, among other, and the relations among them) in addition with the attributes for all of them. The modeling aspects of this ontology are outside of the scope of this work. Having such a structure, the task of bridging the semantic gap is divided in many, less complex, modular and compositional micro-tasks that are which consist in mapping the streaming sensor data using contextual information to the attribute values of the corresponding entities. In this way we can create a structure out of the unstructured, noisy and highly variable sensor data that can then be used by the machine to provide personalized, context-aware services to the final user; • The definition of a reference architecture that applies the methodology above and addresses the semantic gap problem in streaming sensor data; • The instantiation of the architecture above in the Stream Base System (SB), resulting in the implementation of its main components using state-of-the-art software solutions and technologies; • The adoption of the Stream Base System in four use cases that have very different objectives one respect to the other, proving that it works in open domains.
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Zhang, Ping. "Learning from Multiple Knowledge Sources." Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/214795.

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Computer and Information Science
Ph.D.
In supervised learning, it is usually assumed that true labels are readily available from a single annotator or source. However, recent advances in corroborative technology have given rise to situations where the true label of the target is unknown. In such problems, multiple sources or annotators are often available that provide noisy labels of the targets. In these multi-annotator problems, building a classifier in the traditional single-annotator manner, without regard for the annotator properties may not be effective in general. In recent years, how to make the best use of the labeling information provided by multiple annotators to approximate the hidden true concept has drawn the attention of researchers in machine learning and data mining. In our previous work, a probabilistic method (i.e., MAP-ML algorithm) of iteratively evaluating the different annotators and giving an estimate of the hidden true labels is developed. However, the method assumes the error rate of each annotator is consistent across all the input data. This is an impractical assumption in many cases since annotator knowledge can fluctuate considerably depending on the groups of input instances. In this dissertation, one of our proposed methods, GMM-MAPML algorithm, follows MAP-ML but relaxes the data-independent assumption, i.e., we assume an annotator may not be consistently accurate across the entire feature space. GMM-MAPML uses a Gaussian mixture model (GMM) and Bayesian information criterion (BIC) to find the fittest model to approximate the distribution of the instances. Then the maximum a posterior (MAP) estimation of the hidden true labels and the maximum-likelihood (ML) estimation of quality of multiple annotators at each Gaussian component are provided alternately. Recent studies show that it is not the case that employing more annotators regardless of their expertise will result in improved highest aggregating performance. In this dissertation, we also propose a novel algorithm to integrate multiple annotators by Aggregating Experts and Filtering Novices, which we call AEFN. AEFN iteratively evaluates annotators, filters the low-quality annotators, and re-estimates the labels based only on information obtained from the good annotators. The noisy annotations we integrate are from any combination of human and previously existing machine-based classifiers, and thus AEFN can be applied to many real-world problems. Emotional speech classification, CASP9 protein disorder prediction, and biomedical text annotation experiments show a significant performance improvement of the proposed methods (i.e., GMM-MAPML and AEFN) as compared to the majority voting baseline and the previous data-independent MAP-ML method. Recent experiments include predicting novel drug indications (i.e., drug repositioning) for both approved drugs and new molecules by integrating multiple chemical, biological or phenotypic data sources.
Temple University--Theses
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Lazzarini, Nicola. "Knowledge extraction from biomedical data using machine learning." Thesis, University of Newcastle upon Tyne, 2017. http://hdl.handle.net/10443/3839.

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Thanks to the breakthroughs in biotechnologies that have occurred during the recent years, biomedical data is accumulating at a previously unseen pace. In the field of biomedicine, decades-old statistical methods are still commonly used to analyse such data. However, the simplicity of these approaches often limits the amount of useful information that can be extracted from the data. Machine learning methods represent an important alternative due to their ability to capture complex patterns, within the data, likely missed by simpler methods. This thesis focuses on the extraction of useful knowledge from biomedical data using machine learning. Within the biomedical context, the vast majority of machine learning applications focus their e↵ort on the generation and validation of prediction models. Rarely the inferred models are used to discover meaningful biomedical knowledge. The work presented in this thesis goes beyond this scenario and devises new methodologies to mine machine learning models for the extraction of useful knowledge. The thesis targets two important and challenging biomedical analytic tasks: (1) the inference of biological networks and (2) the discovery of biomarkers. The first task aims to identify associations between di↵erent biological entities, while the second one tries to discover sets of variables that are relevant for specific biomedical conditions. Successful solutions for both problems rely on the ability to recognise complex interactions within the data, hence the use of multivariate machine learning methods. The network inference problem is addressed with FuNeL: a protocol to generate networks based on the analysis of rule-based machine learning models. The second task, the biomarker discovery, is studied with RGIFE, a heuristic that exploits the information extracted from machine learning models to guide its search for minimal subsets of variables. The extensive analysis conducted for this dissertation shows that the networks inferred with FuNeL capture relevant knowledge complementary to that extracted by standard inference methods. Furthermore, the associations defined by FuNeL are discovered - 6 - more pertinent in a disease context. The biomarkers selected by RGIFE are found to be disease-relevant and to have a high predictive power. When applied to osteoarthritis data, RGIFE confirmed the importance of previously identified biomarkers, whilst also extracting novel biomarkers with possible future clinical applications. Overall, the thesis shows new e↵ective methods to leverage the information, often remaining buried, encapsulated within machine learning models and discover useful biomedical knowledge.
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Lipton, Zachary C. "Learning from Temporally-Structured Human Activities Data." Thesis, University of California, San Diego, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10683703.

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Despite the extraordinary success of deep learning on diverse problems, these triumphs are too often confined to large, clean datasets and well-defined objectives. Face recognition systems train on millions of perfectly annotated images. Commercial speech recognition systems train on thousands of hours of painstakingly-annotated data. But for applications addressing human activity, data can be noisy, expensive to collect, and plagued by missing values. In electronic health records, for example, each attribute might be observed on a different time scale. Complicating matters further, deciding precisely what objective warrants optimization requires critical consideration of both algorithms and the application domain. Moreover, deploying human-interacting systems requires careful consideration of societal demands such as safety, interpretability, and fairness.

The aim of this thesis is to address the obstacles to mining temporal patterns in human activity data. The primary contributions are: (1) the first application of RNNs to multivariate clinical time series data, with several techniques for bridging long-term dependencies and modeling missing data; (2) a neural network algorithm for forecasting surgery duration while simultaneously modeling heteroscedasticity; (3) an approach to quantitative investing that uses RNNs to forecast company fundamentals; (4) an exploration strategy for deep reinforcement learners that significantly speeds up dialogue policy learning; (5) an algorithm to minimize the number of catastrophic mistakes made by a reinforcement learner; (6) critical works addressing model interpretability and fairness in algorithmic decision-making.

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Varol, Gül. "Learning human body and human action representations from visual data." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE029.

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Le contenu visuel se concentre souvent sur les humains. L’analyse automatique des humains à partir de données visuelles revêt donc une grande importance pour de nombreuses applications. Le but de cette thèse est d’apprendre des représentations visuelles pour l’analyse des humains. Un accent particulier est mis sur deux domaines étroitement liés de la vision artificielle : l’analyse du corps humain et la reconnaissance des actions. En résumé, nos contributions sont les suivantes : (i) nous générons des données synthétiques photoréalistes de personnes permettant l’entraînement de CNNs pour l’analyse du corps humain, (ii) nous proposons une architecture multitâche permettant d’obtenir une représentation volumétrique du corps à partir d’une seule image, (iii) nous étudions les avantages des convolutions temporelles à long terme pour la reconnaissance de l’action humaine à l’aide de CNNs 3D, (iv) nous incorporons une fonction de coût de similarité des vidéos multi-vues pour concevoir des représentations invariantes au changement de vue
The focus of visual content is often people. Automatic analysis of people from visual data is therefore of great importance for numerous applications in content search, autonomous driving, surveillance, health care, and entertainment. The goal of this thesis is to learn visual representations for human understanding. Particular emphasis is given to two closely related areas of computer vision: human body analysis and human action recognition. In summary, our contributions are the following: (i) we generate photo-realistic synthetic data for people that allows training CNNs for human body analysis, (ii) we propose a multi-task architecture to recover a volumetric body shape from a single image, (iii) we study the benefits of long-term temporal convolutions for human action recognition using 3D CNNs, (iv) we incorporate similarity training in multi-view videos to design view-independent representations for action recognition
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Kaithi, Bhargavacharan Reddy. "Knowledge Graph Reasoning over Unseen RDF Data." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1571955816559707.

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Toussaint, Ben-Manson. "Apprentissage automatique à partir de traces multi-sources hétérogènes pour la modélisation de connaissances perceptivo-gestuelles." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAM063/document.

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Les connaissances perceptivo-gestuelles sont difficiles à saisir dans les Systèmes Tutoriels Intelligents. Ces connaissances sont multimodales : elles combinent des connaissances théoriques, ainsi que des connaissances perceptuelles et gestuelles. Leur enregistrement dans les Systèmes Tutoriels Intelligents implique l'utilisation de plusieurs périphériques ou capteurs couvrant les différentes modalités des interactions qui les sous-tendent. Les « traces » de ces interactions –aussi désignées sous le terme "traces d'activité"- constituent la matière première pour la production de services tutoriels couvrant leurs différentes facettes. Les analyses de l'apprentissage ou les services tutoriels privilégiant une facette de ces connaissances au détriment des autres, sont incomplets. Cependant, en raison de la diversité des périphériques, les traces d'activité enregistrées sont hétérogènes et, de ce fait, difficiles à modéliser et à traiter. Mon projet doctoral adresse la problématique de la production de services tutoriels adaptés à ce type de connaissances. Je m'y intéresse tout particulièrement dans le cadre des domaines dits mal-définis. Le cas d'étude de mes recherches est le Système Tutoriel Intelligent TELEOS, un simulateur dédié à la chirurgie orthopédique percutanée. Les propositions formulées se regroupent sous trois volets : (1) la formalisation des séquences d'interactions perceptivo-gestuelles ; (2) l'implémentation d'outils capables de réifier le modèle conceptuel de leur représentation ; (3) la conception et l'implémentation d'outils algorithmiques favorisant l'analyse de ces séquences d'un point de vue didactique
Perceptual-gestural knowledge is multimodal : they combine theoretical and perceptual and gestural knowledge. It is difficult to capture in Intelligent Tutoring Systems. In fact, its capture in such systems involves the use of multiple devices or sensors covering all the modalities of underlying interactions. The "traces" of these interactions -also referred to as "activity traces"- are the raw material for the production of key tutoring services that consider their multimodal nature. Methods for "learning analytics" and production of "tutoring services" that favor one or another facet over others, are incomplete. However, the use of diverse devices generates heterogeneous activity traces. Those latter are hard to model and treat.My doctoral project addresses the challenge related to the production of tutoring services that are congruent to this type of knowledge. I am specifically interested to this type of knowledge in the context of "ill-defined domains". My research case study is the Intelligent Tutoring System TELEOS, a simulation platform dedicated to percutaneous orthopedic surgery.The contributions of this thesis are threefold : (1) the formalization of perceptual-gestural interactions sequences; (2) the implementation of tools capable of reifying the proposed conceptual model; (3) the conception and implementation of algorithmic tools fostering the analysis of these sequences from a didactic point of view
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Books on the topic "Data and human knowledge learning"

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Stefanie, Lindstaedt, Kloos Carlos Delgado, Hernández-Leo Davinia, and SpringerLink (Online service), eds. 21st Century Learning for 21st Century Skills: 7th European Conference of Technology Enhanced Learning, EC-TEL 2012, Saarbrücken, Germany, September 18-21, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Spiliopoulou, Myra, Lars Schmidt-Thieme, and Ruth Janning, eds. Data Analysis, Machine Learning and Knowledge Discovery. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01595-8.

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Lausen, Berthold, Sabine Krolak-Schwerdt, and Matthias Böhmer, eds. Data Science, Learning by Latent Structures, and Knowledge Discovery. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-44983-7.

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Chadha, Gaurav. e-Learning: An expression of the knowledge economy. New Delhi: Tata McGraw-Hill, 2002.

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Chadha, Gaurav. e-Learning: An expression of the knowledge economy. New Delhi: Tata McGraw-Hill, 2002.

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Conference on Data Analysis, Learning Symbolic and Numeric Knowledge (1989 Antibes, France). Data analysis, learning symbolic and numeric knowledge: Proceedings of the Conference on Data Analysis, Learning Symbolic and Numeric Knowledge, Antibes, September 11-14, 1989. Commack, N.Y: Nova Science Publishers, 1989.

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E, Diday, and Institut national de recherche en informatique et en automatique (France), eds. Data analysis, learning symbolic and numeric knowledge: Proceedings of the Conference on Data Analysis, Learning Symbolic and Numeric Knowledge, Antibes, September 11-14, 1989. Commack, N.Y: Nova Science Publishers, 1989.

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Sallis, Edward. Knowledge management in education: Enhancing learning & education. London: Kogan Page, 2002.

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Dong, Yuxiao, Nicolas Kourtellis, Barbara Hammer, and Jose A. Lozano, eds. Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86517-7.

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Dong, Yuxiao, Nicolas Kourtellis, Barbara Hammer, and Jose A. Lozano, eds. Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86514-6.

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Book chapters on the topic "Data and human knowledge learning"

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Oikarinen, Emilia, Kai Puolamäki, Samaneh Khoshrou, and Mykola Pechenizkiy. "Supervised Human-Guided Data Exploration." In Machine Learning and Knowledge Discovery in Databases, 85–101. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43823-4_8.

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Mitchell, Tom. "Machine Learning for Analyzing Human Brain Function." In Advances in Knowledge Discovery and Data Mining, 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11430919_1.

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Henelius, Andreas, Emilia Oikarinen, and Kai Puolamäki. "Tiler: Software for Human-Guided Data Exploration." In Machine Learning and Knowledge Discovery in Databases, 672–76. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10997-4_49.

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Freitas, Leandro O., Pedro R. Henriques, and Paulo Novais. "Knowledge Inference Through Analysis of Human Activities." In Intelligent Data Engineering and Automated Learning – IDEAL 2019, 274–81. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33607-3_30.

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Osmani, Aomar, Massinissa Hamidi, and Pegah Alizadeh. "Hierarchical Learning of Dependent Concepts for Human Activity Recognition." In Advances in Knowledge Discovery and Data Mining, 79–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75765-6_7.

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Andrade, Thiago, Brais Cancela, and João Gama. "Mining Human Mobility Data to Discover Locations and Habits." In Machine Learning and Knowledge Discovery in Databases, 390–401. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43887-6_32.

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Yin, Jie, Son N. Tran, and Qing Zhang. "Human Identification via Unsupervised Feature Learning from UWB Radar Data." In Advances in Knowledge Discovery and Data Mining, 322–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93034-3_26.

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Xu, Zhen, and Binheng Song. "A Machine Learning Application for Human Resource Data Mining Problem." In Advances in Knowledge Discovery and Data Mining, 847–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11731139_99.

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Huang, Heng, Yunhan Bai, Hongwei Liang, and Xiaozhong Liu. "IR Embedding Fairness Inspection via Contrastive Learning and Human-AI Collaborative Intelligence." In Advances in Knowledge Discovery and Data Mining, 142–53. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2238-9_11.

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Mercep, Ljubo, Gernot Spiegelberg, and Alois Knoll. "Human Performance Profiling While Driving a Sidestick-Controlled Car." In Data Science, Learning by Latent Structures, and Knowledge Discovery, 455–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-44983-7_40.

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Conference papers on the topic "Data and human knowledge learning"

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Reddy, Siddharth, Igor Labutov, Siddhartha Banerjee, and Thorsten Joachims. "Unbounded Human Learning." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2939672.2939850.

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Feng, Jie, Zeyu Yang, Fengli Xu, Haisu Yu, Mudan Wang, and Yong Li. "Learning to Simulate Human Mobility." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3412862.

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Hwang, Kevin, Sai Challagundla, Maryam Alomair, Doug Janssen, Kendall Morton, Lujie Chen, and Fow-Sen Choa. "Towards the acceleration of human learning capabilities through AI-assisted knowledge-tree building." In Big Data VI: Learning, Analytics, and Applications, edited by Panos P. Markopoulos. SPIE, 2024. http://dx.doi.org/10.1117/12.3013103.

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Corvino, Gabriel, Vitor Vasconcelos Oliveira, Angelo C. Mendes da Silva, and Ricardo Marcondes Marcacini. "On the use of Query by Committee for Human-in-the-Loop Named Entity Recognition." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/kdmile.2022.227953.

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Named Entity Recognition is a relevant task for extracting information from textual data. Traditional methods for training NER models assume that humans annotate entities manually, identifying entities in predefined categories. This strategy presents a great human effort, mainly in more specific application domains. To address these challenges, we consider Human in the Loop (HITL), which can be understood as a set of strategies to incorporate human knowledge and experience into machine learning, while accelerating model training. In this paper, we investigate a classic method called Query by Committee (QBC), which helps to select informative instances for data labeling. Traditionally, QBC selects instances with a high level of disagreement between different models of a committee. We present heuristics for QBC relaxation to also consider some level of agreement. We showed that taking advantage of some committee agreement for pre-labeling of instances is promising to speed up human feedback and increase the training set. Experimental results showed that our method is able to preserve the performance of models compared to traditional QBC, while reducing human labeling effort.
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Manoharan, Geetha, Jalaja V, Manoj A. Sathe, Neetika, Melanie Lourens, and K. Suresh. "Machine Learning and Data Privacy in Human Resource Management." In 2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM). IEEE, 2023. http://dx.doi.org/10.1109/iccakm58659.2023.10449576.

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Guestrin, Carlos. "4 Perspectives in Human-Centered Machine Learning." In KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3292500.3340399.

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Cha, Inha, Juhyun Oh, Cheul Young Park, Jiyoon Han, and Hwalsuk Lee. "Unlocking the Tacit Knowledge of Data Work in Machine Learning." In CHI '23: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3544549.3585616.

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Saisho, Osamu, Takeshi Ohguro, Jingyu Sun, Hiroshi Imamura, Susumu Takeuchi, and Daigoro Yokozeki. "Human Knowledge Based Efficient Interactive Data Annotation via Active Weakly Supervised Learning." In 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 2021. http://dx.doi.org/10.1109/percomworkshops51409.2021.9431067.

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Nasiriyan, Fariba, and Hassan Khotanlou. "Human detection in laser range data using deep learning and 3-D objects." In 2015 7th Conference on Information and Knowledge Technology (IKT). IEEE, 2015. http://dx.doi.org/10.1109/ikt.2015.7288748.

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Zhang, Dongtian, Weiwei Tian, Yifan Yin, Xiufeng Liu, Xu Cheng, and Fan Shi. "Human Knowledge-based Compressed Federated Learning Model for Wind Turbine Blade Icing Detection." In 2022 International Conference on High Performance Big Data and Intelligent Systems (HDIS). IEEE, 2022. http://dx.doi.org/10.1109/hdis56859.2022.9991642.

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Reports on the topic "Data and human knowledge learning"

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Shrestha, Tanuja, Mir A. Matin, Vishwas Chitale, and Samuel Thomas. Exploring the potential of deep learning for classifying camera trap data: A case study from Nepal - working paper. International Centre for Integrated Mountain Development (ICIMOD), September 2023. http://dx.doi.org/10.53055/icimod.1016.

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Data from camera trap networks provide crucial information on various important aspects of wildlife presence, movement, and behaviour. However, manual processing of large volumes of images captured is time and resource intensive. This study explores three different approaches of deep learning methods to detect and classify images of key animal species collected from the ICIMOD Knowledge Park at Godavari, Nepal. It shows that transfer learning with ImageNet pretrained models (A1) can be used to detect animal species with minimal model training and testing. These methods when scaled up offer tremendous scope for quicker and informed conflict management actions, including automated response, which can help minimise human wildlife conflict management costs across countries in the region.
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Way, L., S. West, B. Swift, L. Whatford, and C. Rymer. Learnings from the pilot Citizen Science and AMR project. Food Standards Agency, November 2023. http://dx.doi.org/10.46756/sci.fsa.axj107.

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Antimicrobial resistance (AMR) is a global societal challenge which can be characterised as a ‘One Health’ problem as it has implications not only for human health but also that of animals, the environment and, ultimately, the economy. Despite the significance of this threat, there remain substantial knowledge gaps in relation to transmission pathways for AMR within the food system, and home-growing is a particularly understudied space. Citizen Science and Antimicrobial Resistance (CSAMR) was a pilot project designed to collate data on the cultivation and food preparation practices of home-growers which could enrich existing knowledge on how AMR bacteria move through the food system. CSAMR sought equally to prove the efficacy of citizen science methodology to contribute to the evidence base in this research area.
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Belokonova, Nadezhda, Elena Ermishina, Natalya Kataeva, Natalia Naronova, and Kristina Golitsyna. E-learning course "Chemistry". SIB-Expertise, January 2024. http://dx.doi.org/10.12731/er0770.29012024.

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The electronic training course ""Chemistry"" was created as an auxiliary resource to accompany the chemistry curriculum for the specialties of General Medicine, Pediatrics, and Dentistry. The purpose of studying the course is to form ideas about the structure and transformations of organic and inorganic substances that underlie life processes and influence these processes, in direct connection with the biological functions of these compounds. Course objectives: - formation of knowledge and skills about the basic laws of thermodynamics and bioenergy; about the structure and chemical properties of bioorganic compounds and their derivatives; - formation of knowledge necessary when considering the physical and chemical essence of processes occurring in the human body at the molecular and cellular levels; - developing the ability to carry out, when necessary, calculations of the parameters of these processes, which will allow a deeper understanding of the functions of individual systems of the body and the body as a whole, as well as its interaction with the environment; - training of a specialist who has a sufficient level of knowledge, skills, abilities, and is able to think independently and be interested in research work. The labor intensity of the course is 108 hours. The course consists of 3 didactic units. Each course topic contains theoretical material, a practice test to test your understanding of the theory, and a final test. Each final test on a topic is equivalent to a control event according to a point-rating system. Laboratory work is presented in the form of a video file and a test for it. In this way, an electronic form of completing a report for laboratory work is carried out. The materials presented in the course can be used by teachers as basic when testing students or as additional to those methodological developments that are currently used at the department.
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Rau, Jane. PR-580-163710-R01 Determining the Impact of Human Factors in the Performance of In-Service NDE. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), January 2020. http://dx.doi.org/10.55274/r0011651.

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The term "Human Factors" has become a buzz phrase in the pipeline industry, and rightfully so. Root cause analyses of many failures point to human error as the cause. However, in the pipeline industry, "Human Factors" generally refers to ergonomics, fatigue, stress, environment, and communication. JTrain, Inc.'s research looks at Human Factors from an educational viewpoint; how is transfer of knowledge from Subject Matter Experts (SMEs) and Level IIIs certified technicians to Non Destructive Examination (NDE) technicians presently performed, and how can it be improved to ensure actual learning occurs and not just rote memorization to pass a test. Being a knowledgeable SME or Level III does not guarantee an ability to teach or an understanding of how learning is best achieved. This report discusses how research-based learning strategies and best practices in teaching and learning can improve transfer of knowledge and competence for NDE technicians.
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Kataeva, Natalya, Natalia Naronova, and Kristina Golitsyna. E-learning course "Bioorganic chemistry". Федеральное государственное бюджетное образовательное учреждение высшего образования "Уральский государственный медицинский университет" Министерства здравоохранения Российской Федерации, December 2024. https://doi.org/10.12731/er0857.12122024.

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The electronic training course ""Organic Chemistry"" was created as an auxiliary resource to accompany the curriculum of bioorganic chemistry for the specialties of Medical and Preventive Care. The purpose of studying the course is to form ideas about the spatial structure, reactivity of bioorganic substances and their biological significance for the most important processes in human life. Course objectives: formation of knowledge about the structure of the main classes of bioorganic compounds; formation of skills in writing the main chemical reactions of bioorganic compounds; formation of skills in qualitative and quantitative reactions for the analysis of various classes of bioorganic compounds; formation of experimental skills necessary in future professional activities. The course labor intensity is 108 hours. The course consists of 3 didactic units. Each topic of the course ""Organic Chemistry"" contains theoretical material, a training test to check the assimilation of the theory, and a final test. Each final test on the topic is equivalent to a control event according to the point-rating system. Laboratory work consists of a video file and a video test. In this way, laboratory work is carried out and the assimilation of theoretical and practical material is monitored.
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Oskolkov, Nikolay. Machine Learning for Computational Biology. Instats Inc., 2024. http://dx.doi.org/10.61700/l01vi14ohm8en1490.

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This one-day workshop, led by Nikolay Oskolkov from Lund University, provides a comprehensive introduction to machine learning techniques in computational biology, focusing on both theoretical knowledge and practical coding skills in R and Python. Participants will learn to implement from scratch and optimize algorithms such as neural networks, random forest, k-means clustering, and Markov Chain Monte Carlo (MCMC), making it an essential resource for advancing research in biostatistics, genetics, and data science.
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Fang, Mei Lan, Lupin Battersby, Marianne Cranwell, Heather Cassie, Moya Fox, Philippa Sterlini, Jenna Breckenridge, Alex Gardner, and Thomas Curtin. IKT for Research Stage 5: Data Collection. University of Dundee, December 2022. http://dx.doi.org/10.20933/100001252.

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In 2020, the University of Dundee initiated the development of an Open Research strategy. As part of this initiative, in February 2021 the University’s Library and Learning Centre together with Open Research Champions from the Schools of Health Sciences and Dentistry, formed an Open Research Working group. To build on the University’s open research policy and infrastructure, the purpose of the group was to facilitate ongoing research and development of best practice approaches for our interdisciplinary environment to make outputs, data and other products of our research publicly available, building on University of Dundee’s Open Research policy and infrastructure. Through informal consultations with academic staff and students, the Open Research Working Group found that: → access and reach of research findings can be amplified through effective knowledge mobilisation, and stakeholder and patient and public involvement; and → there was a need for guidance and resources on how-to implement knowledge mobilisation activities with and for stakeholders throughout the entire research process – from proposal development to project completion. In June 2021, the Open Research working group, in partnership with Simon Fraser University’s Knowledge Mobilization Hub began the development of an Integrated Knowledge Translation (IKT) Toolkit, with funding support from the University of Dundee’s Doctoral Academy and Organisational Professional Development. IKT is an approach to knowledge translation that emphasises working in an engaged and collaborative partnership with stakeholders throughout the research cycle in order to have positive impact. The aim was to co-produce evidence-informed, best practice learning materials on how-to: → maintain ongoing relationships between researchers, community stakeholders and decision-makers in research development and implementation; and → facilitate an integrated, participatory way of knowledge production whereby researchers, practitioners and other knowledge users can collaborate to co-generate new and accessible knowledge that can be utilised in contexts ranging from supporting community development to policy guidance for practice. The IKT Toolkit was informed by a focused evidence review and synthesis of published peerreviewed and grey literature and consists of 8 knowledge briefs and a slide deck co-produced for use in any discipline or sector. Each knowledge brief provides practical guidance and resources to support an IKT process in each of eight key research stages: (i) Partnership Building; (ii) Generating Priorities and Ideas; (iii) Proposal development; (iv) Study Design; (v) Data Collection; (vi) Data Analysis; (vii) Reporting and (viii) Dissemination. The current knowledge brief provides IKT guidance on Research Stage 5: Data Collection.
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Fang, Mei Lan, Lupin Battersby, Marianne Cranwell, Heather Cassie, Moya Fox, Philippa Sterlini, Jenna Breckenridge, Alex Gardner, and Thomas Curtin. IKT for Research Stage 6: Data Analysis. University of Dundee, December 2022. http://dx.doi.org/10.20933/100001253.

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Abstract:
In 2020, the University of Dundee initiated the development of an Open Research strategy. As part of this initiative, in February 2021 the University’s Library and Learning Centre together with Open Research Champions from the Schools of Health Sciences and Dentistry, formed an Open Research Working group. To build on the University’s open research policy and infrastructure, the purpose of the group was to facilitate ongoing research and development of best practice approaches for our interdisciplinary environment to make outputs, data and other products of our research publicly available, building on University of Dundee’s Open Research policy and infrastructure. Through informal consultations with academic staff and students, the Open Research Working Group found that: → access and reach of research findings can be amplified through effective knowledge mobilisation, and stakeholder and patient and public involvement; and → there was a need for guidance and resources on how-to implement knowledge mobilisation activities with and for stakeholders throughout the entire research process – from proposal development to project completion. In June 2021, the Open Research working group, in partnership with Simon Fraser University’s Knowledge Mobilization Hub began the development of an Integrated Knowledge Translation (IKT) Toolkit, with funding support from the University of Dundee’s Doctoral Academy and Organisational Professional Development. IKT is an approach to knowledge translation that emphasises working in an engaged and collaborative partnership with stakeholders throughout the research cycle in order to have positive impact. The aim was to co-produce evidence-informed, best practice learning materials on how-to: → maintain ongoing relationships between researchers, community stakeholders and decision-makers in research development and implementation; and → facilitate an integrated, participatory way of knowledge production whereby researchers, practitioners and other knowledge users can collaborate to co-generate new and accessible knowledge that can be utilised in contexts ranging from supporting community development to policy guidance for practice. The IKT Toolkit was informed by a focused evidence review and synthesis of published peer-reviewed and grey literature and consists of 8 knowledge briefs and a slide deck co-produced for use in any discipline or sector. Each knowledge brief provides practical guidance and resources to support an IKT process in each of eight key research stages: (i) Partnership Building; (ii) Generating Priorities and Ideas; (iii) Proposal development; (iv) Study Design; (v) Data Collection; (vi) Data Analysis; (vii) Reporting and (viii) Dissemination. The current knowledge brief provides IKT guidance on Research Stage 6: Data Analysis.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Millican, Juliet. Civil Society Learning Journey Briefing Note 3: Methods for Supporting or Countering Informal Social Movements. Institute of Development Studies, October 2022. http://dx.doi.org/10.19088/k4d.2022.153.

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In 2018 key concerns included shrinking civic space and the impact of this on democracy. Developments between the two periods, particularly the COVID-19 pandemic, the Black Lives Matter and decolonisation movements, have only increased emphasis on commitments made as part of the Grand Bargain to localise and decolonise. This invariably means working more frequently with local partners and civil society organisations in the delivery of international aid to advance Open Society and Human Rights agendas. These three briefing notes summarise key considerations emerging from the ‘Working with Civil Society’ Learning Journey facilitated for the Foreign, Commonwealth and Development Office (FCDO) as part of the Knowledge, Evidence and Learning for Development (K4D) Programme.
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