Academic literature on the topic 'Machine learning interactif'
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Journal articles on the topic "Machine learning interactif"
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Chavel, Thierry. "La rencontre humaine est-elle soluble dans l’intelligence artificielle ?" Management international 28, no. 2 (2024): 142–44. http://dx.doi.org/10.59876/a-ma53-q5cw.
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Avec la numérisation du monde, la réalité n’est plus ce qu’elle était. Je peux avoir l’illusion d’être ici et ailleurs. Un cloud remplace ma mémoire personnelle. L’autre du débat s’efface au profit du même des communautés virtuelles. La 4e révolution industrielle n’est pas qu’un saut technologique, c’est surtout un choix de société qui renouvelle en profondeur l’exercice du leadership et ses trois fondements humanistes : la fragilité, l’altérité et la responsabilité. L’irruption d’outils de machine learning tels que Chat-GPT transforme violemment les métiers de la prestation intellectuelle. Un algorithme sophistiqué peut désormais produire un langage cohérent, vraisemblable et interactif. A l’image des avocats pressés, des recruteurs en batterie et des collégiens paresseux, la tribu coach voit muter sa liturgie de la présence. Concrètement, quelle place l’intelligence artificielle (IA) va-t-elle prendre dans l’accompagnement humain ?
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Li, Jiahang. "Research on Interactive System of Movie Subtitle Speech Based on Machine Learning Technology." Frontiers in Computing and Intelligent Systems 2, no. 2 (December 26, 2022): 22–24. http://dx.doi.org/10.54097/fcis.v2i2.3744.
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The composition elements of subtitles, from the early single text, have developed into the present text, graphics, colors, animation, special effects and other combinations. With the development of speech technology and natural language understanding, speech interaction system has become a hot research field. Different from the traditional data interaction between keyboard, mouse and display, using hearing to transmit data makes the interactive system of movie subtitles more anthropomorphic and intelligent. It is the most natural and convenient means for human beings to exchange information with intelligent systems by incorporating machines and equipment with voice information processing capabilities into human voice interactive objects and endowing movie subtitle interactive systems with biological language recognition functions. A machine learning-based movie subtitle voice interactive system is constructed, which can well expand the application of voice interactive system and improve the user experience. In this paper, a movie subtitle voice interactive system based on machine learning technology is proposed, so as to better and effectively realize human-computer voice interaction.
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Animesh, Kumar, and Dr Srikanth V. "Enhancing Healthcare through Human-Robot Interaction using AI and Machine Learning." International Journal of Research Publication and Reviews 5, no. 3 (March 21, 2024): 184–90. http://dx.doi.org/10.55248/gengpi.5.0324.0831.
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An, Chang. "Student Status Supervision in Ideological and Political Machine Teaching Based on Machine Learning." E3S Web of Conferences 275 (2021): 03028. http://dx.doi.org/10.1051/e3sconf/202127503028.
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Under the premise of active in the field of machine learning, this paper takes online teaching system of ideological and Political education as an example to study machine learning and machine teaching system. In order to specifically understand the current situation of the construction and application of machine teaching based on supervised teaching of ideological and political theory courses in local colleges and universities, this experiment first conducted a statistical analysis of the learning results of the surveyed classes in two semesters from March 2020 to December 2020. The experimental data show that there is a positive interaction between teachers and students. Most students use the interactive communication mode of machines, while a small number of students use real-time interactive discussions with teachers. The experimental results show that the excellent rate of ABC classes in the first semester is 80%, 82% and 90%, respectively, through the machine-supervised teaching mode. Therefore, supervised machine learning can greatly help students improve their academic performance. In the future, we should further explore the application of other personalized and extensible machine learning methods in quality education.
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Amershi, Saleema, James Fogarty, Ashish Kapoor, and Desney Tan. "Effective End-User Interaction with Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 1529–32. http://dx.doi.org/10.1609/aaai.v25i1.7964.
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End-user interactive machine learning is a promising tool for enhancing human productivity and capabilities with large unstructured data sets. Recent work has shown that we can create end-user interactive machine learning systems for specific applications. However, we still lack a generalized understanding of how to design effective end-user interaction with interactive machine learning systems. This work presents three explorations in designing for effective end-user interaction with machine learning in CueFlik, a system developed to support Web image search. These explorations demonstrate that interactions designed to balance the needs of end-users and machine learning algorithms can significantly improve the effectiveness of end-user interactive machine learning.
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Guo, Chao-Yu, and Ke-Hao Chang. "A Novel Algorithm to Estimate the Significance Level of a Feature Interaction Using the Extreme Gradient Boosting Machine." International Journal of Environmental Research and Public Health 19, no. 4 (February 18, 2022): 2338. http://dx.doi.org/10.3390/ijerph19042338.
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Recent studies have revealed the importance of the interaction effect in cardiac research. An analysis would lead to an erroneous conclusion when the approach failed to tackle a significant interaction. Regression models deal with interaction by adding the product of the two interactive variables. Thus, statistical methods could evaluate the significance and contribution of the interaction term. However, machine learning strategies could not provide the p-value of specific feature interaction. Therefore, we propose a novel machine learning algorithm to assess the p-value of a feature interaction, named the extreme gradient boosting machine for feature interaction (XGB-FI). The first step incorporates the concept of statistical methodology by stratifying the original data into four subgroups according to the two interactive features. The second step builds four XGB machines with cross-validation techniques to avoid overfitting. The third step calculates a newly defined feature interaction ratio (FIR) for all possible combinations of predictors. Finally, we calculate the empirical p-value according to the FIR distribution. Computer simulation studies compared the XGB-FI with the multiple regression model with an interaction term. The results showed that the type I error of XGB-FI is valid under the nominal level of 0.05 when there is no interaction effect. The power of XGB-FI is consistently higher than the multiple regression model in all scenarios we examined. In conclusion, the new machine learning algorithm outperforms the conventional statistical model when searching for an interaction.
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Spillard, Samuel, Christopher J. Turner, and Konstantinos Meichanetzidis. "Machine learning entanglement freedom." International Journal of Quantum Information 16, no. 08 (December 2018): 1840002. http://dx.doi.org/10.1142/s0219749918400026.
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Quantum many-body systems realize many different phases of matter characterized by their exotic emergent phenomena. While some simple versions of these properties can occur in systems of free fermions, their occurrence generally implies that the physics is dictated by an interacting Hamiltonian. The interaction distance has been successfully used to quantify the effect of interactions in a variety of states of matter via the entanglement spectrum [C. J. Turner, K. Meichanetzidis, Z. Papic and J. K. Pachos, Nat. Commun. 8 (2017) 14926, Phys. Rev. B 97 (2018) 125104]. The computation of the interaction distance reduces to a global optimization problem whose goal is to search for the free-fermion entanglement spectrum closest to the given entanglement spectrum. In this work, we employ techniques from machine learning in order to perform this same task. In a supervised learning setting, we use labeled data obtained by computing the interaction distance and predict its value via linear regression. Moving to a semi-supervised setting, we train an autoencoder to estimate an alternative measure to the interaction distance, and we show that it behaves in a similar manner.
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Kumar, Dr Tribhuwan, Klinge Orlando Villalba-Condori, Dennis Arias-Chavez, Rajesh K., Kalyan Chakravarthi M, and Dr Suman Rajest S. "An Evaluation on Speech Recognition Technology based on Machine Learning." Webology 19, no. 1 (January 20, 2022): 646–63. http://dx.doi.org/10.14704/web/v19i1/web19046.
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Speech is the basic way of interaction between the listener to the speaker by voice or expression. Humans can easily understand the speakers' message, but machines can't understand the speaker's word. Nowadays, most of our lives are occupied by machines; but we can't interact with machines. The human brain, like machine learning technology, is essential for speech recognition to interact with machines to humans. The language used for speech recognition must be a global language, so English has been used in this paper. The machine learning methodology is used in a lot of assignments through the feature learning capability. The data modelling capability results attained supplementary than the performance of normal learning methodology. So, in this work, the speech signal recognition is based on a machine-learning algorithm to merge the speech features and attributes. As a result of voice as a bio-metric implication, the speech signal is converted into a significant element of speech improvement. A new speech and emotion recognition technology is introduced. In this paper, discriminated speaking technology are spotlighted on the feature extraction, improvement, segmentation and progression of speech emotion recognition. Initially, the trained RNN layer-based feature extraction is done to get the speech signal's high-level features. From the generated high-level features are used for generating the new speech feature for the capsule network. Finally, the obtained speech features and attribute features are combined into the same RNN with Caps Net framework through the fully connected network. The experimental result shows the improved proposed speech recognition algorithms accuracy with another state-of-the-art method.
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Coe, J. P. "Machine Learning Configuration Interaction." Journal of Chemical Theory and Computation 14, no. 11 (October 4, 2018): 5739–49. http://dx.doi.org/10.1021/acs.jctc.8b00849.
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Holzinger, Andreas. "Interactive Machine Learning (iML)." Informatik-Spektrum 39, no. 1 (November 29, 2015): 64–68. http://dx.doi.org/10.1007/s00287-015-0941-6.
Full textDissertations / Theses on the topic "Machine learning interactif"
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Scurto, Hugo. "Designing With Machine Learning for Interactive Music Dispositifs." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS356.
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La musique est une pratique culturelle permettant aux êtres humains d'exprimer sensiblement leurs intentions à travers le son. L'apprentissage machine définit un ensemble de modèles permettant de nouvelles formes d'expression au sein desdits systèmes interactifs musicaux. Cependant, en tant que discipline informatique, l'apprentissage machine demeure essentiellement appliquée à la musique du point de vue des sciences de l'ingénieur, qui, très souvent, conçoit les modèles d'apprentissage sans tenir compte des interactions musicales prenant place entre humains et systèmes. Dans cette thèse, j'envisage la possibilité de mener des pratiques de design avec l'apprentissage machine pour les systèmes interactifs musicaux. Je soutiens que l'apprentissage machine doit avant tout être situé au sein d'un contexte humain afin d'être conçu et appliqué au design de systèmes interactifs musicaux. Pour défendre cette thèse, je présente quatre études interdisciplinaires, dans lesquelles j'introduis des modèles intermédiaires d'apprentissage, dits modèles-prototype, au sein de méthodes de conception centrées humain, afin d'appliquer l'apprentissage machine à quatre tâches musicales situées : le mapping mouvement-son, l'exploration sonore, l'exploration de la synthèse, et l'interaction musicale collective. À travers ces études, je montre que les modèles-prototype permettent de générer des idées de design pour l'apprentissage machine en amont de la phase d'ingénierie desdits modèles, ce en lien étroit avec les utilisateurs potentiels de ces systèmes. Je montre également que les systèmes d'apprentissage machine centrés humain résultant de ce processus de conception rendent possible des processus dynamiques d'expression entre les humains et les machines, allant au-delà de la création d'artefacts musicaux statiques. Je propose de nommer co-expression ces processus d'interaction musicale entre des êtres humains - faisant preuve d'un élan expressif et créatif quelque soit leur expertise musicale - et des machines - dont les capacités d'apprentissage peuvent être perçues comme expressives du point de vue de l'humain. En outre, je présente cinq systèmes interactifs musicaux conçus avec lesdits modèles-prototypes, et relate leurs restitutions publiques au sein d'ateliers, expositions, installations et performances. Par une approche réflexive, je montre que les contributions musicales apportées par des pratiques de design avec l'apprentissage machine peuvent, à terme, complémenter les contributions scientifiques apportées par les méthodes de conception centrées humain. Ainsi, je suggère que la recherche musicale peut être menée par le design de dispositifs interactifs musicaux, c'est-à-dire, par la réalisation technique d'artefacts esthétiquement fonctionnels remettant en cause les normes culturelles régissant l'informatique et la musiqueMusic is a cultural and creative practice that enables humans to express a variety of feelings and intentions through sound. Machine learning opens many prospects for designing human expression in interactive music systems. Yet, as a Computer Science discipline, machine learning remains mostly studied from an engineering sciences perspective, which often exclude humans and musical interaction from the loop of the created systems. In this dissertation, I argue in favour of designing with machine learning for interactive music systems. I claim that machine learning must be first and foremost situated in human contexts to be researched and applied to the design of interactive music systems. I present four interdisciplinary studies that support this claim, using human-centred methods and model prototypes to design and apply machine learning to four situated musical tasks: motion-sound mapping, sonic exploration, synthesis exploration, and collective musical interaction. Through these studies, I show that model prototyping helps envision designs of machine learning with human users before engaging in model engineering. I also show that the final human-centred machine learning systems not only helps humans create static musical artifacts, but supports dynamic processes of expression between humans and machines. I call co-expression these processes of musical interaction between humans - who may have an expressive and creative impetus regardless of their expertise - and machines - whose learning abilities may be perceived as expressive by humans. In addition to these studies, I present five applications of the created model prototypes to the design of interactive music systems, which I publicly demonstrated in workshops, exhibitions, installations, and performances. Using a reflexive approach, I argue that the musical contributions enabled by such design practice with machine learning may ultimately complement the scientific contributions of human-centred machine learning. I claim that music research can thus be led through dispositif design, that is, through the technical realization of aesthetically-functioning artifacts that challenge cultural norms on computer science and music
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Gosselin, Philippe-Henri. "Apprentissage interactif pour la recherche par le contenu dans les bases multimédias." Habilitation à diriger des recherches, Université de Cergy Pontoise, 2011. http://tel.archives-ouvertes.fr/tel-00660316.
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Les bases actuelles de données multimédia nécessitent des outils de plus en plus avancés pour pouvoir être parcourues avec efficacité. Dans ce contexte, la recherche en interaction avec un utilisateur est une approche qui permet de résoudre des requêtes à la sémantique complexe avec rapidité, sans pour autant nécessiter un haut niveau d'expertise utilisateur. Parmi les différents éléments intervenant dans la conception d'un système de recherche interactive, deux parties essentielles interviennent: l'indexation et la similarité entre les documents multimédia, et la gestion du processus interactif. Dans le contexte de la recherche multimédia par le contenu, on s'appuie sur des descriptions visuelles extraites automatiquement des documents. Suite à cette étape initiale, il est nécessaire de produire des structures de données, appelées index, ainsi qu'une métrique capable de comparer ces structures. Pour ce faire, nous proposons de représenter un document sous la forme d'un graphe, où chaque sommet du graphe représente une partie du document (région, point d'intérêt, ...) et chaque arête du graphe représente une relation entre deux parties du document. Puis, nous introduisons des métriques associées, sous la forme de fonctions noyaux sur graphes, qui permettent d'utiliser ces représentations complexes avec les méthodes d'apprentissages Hilbertiennes, telle que les SVMs. La gestion du processus interactif entre le système et un utilisateur a fait d'important progrès grâce à l'approche dite par apprentissage actif. Les premières approches proposent des critères pertinents pour la sélection de document à faire annoter par l'utilisateur, dans le but de trouver les documents recherchés au plus vite. Dans ce contexte, nous proposons d'aller plus loin en nous intéressant à la fabrication "en ligne" d'index et métriques associées en fonction de la nature de la recherche. En d'autres termes, nous proposons de remplacer le schéma traditionnel, où un unique index général est utilisé, par un schéma d'indexation active, où chaque utilisateur dispose d'un index dédié à sa requête.
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Sungeelee, Vaynee. "Human-Machine Co-Learning : interactive curriculum generation for the acquisition of motor skills." Electronic Thesis or Diss., Sorbonne université, 2024. https://theses.hal.science/tel-04828514.
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L'acquisition de compétences motrices est le processus par lequel une personne est capable d'exécuter un mouvement avec plus de précision. Dans ce contexte, la pratique joue un rôle déterminant ; elle contribue à améliorer les performances de l'apprenant, mais n'y est pas toujours adapté. Un moyen de personnaliser l'apprentissage est de créer des séquences d'apprentissage qui conviennent à l'apprenant. Cependant, créer ces séquences manuellement demande du temps, ce qui rend cette démarche peu pratique. La génération automatique de séquences d'apprentissage peut remédier à ce problème. Les stratégies adaptatives qui structurent l'entraînement, c'est-à-dire la séquence de tâche à effectuer, en tenant compte de la difficulté de la tâche et du niveau de compétence de l'individu ont ainsi le potentiel d'améliorer l'apprentissage moteur. Ce double processus d'apprentissage par une machine de la séquence adaptée à un apprenant humain est ce que nous appelons le co-apprentissage. Dans cette thèse, nous abordons le co-apprentissage humain-machine dans le cas de l'apprentissage moteur, c'est-à-dire où les séquences d'apprentissage sont apprises en même temps que l'humain apprend à faire la tâche motrice.Les algorithmes de Machine Learning sont capables d'analyser les tendances d'apprentissage de chaque apprenant et ainsi adapter les consignes d'entraînement en fonction de ses besoins. Ils peuvent aussi être utilisés pour contrôler une interface homme-machine, dans un contexte où l'humain apprend à adapter ses mouvements (par exemple, pour apprendre à contrôler une prothèse). Dans cette thèse, le Machine Learning est utilisé afin de faciliter l'acquisition d'habiletés motrices. Cependant, une approche fondée sur le Machine Learning se heurte à de nombreuses exigences : (i) peu de données sont disponibles pour entraîner les algorithmes, (ii) la nature interactive du système implique un entraînement de courte durée (iii) l'efficacité des algorithmes dépend d'une évaluation précise du niveau de compétence de l'apprenant, qui est difficile à mesurer en pratique et (iv) le degré de contrôle que l'humain exerce sur l'entraînement du modèle de Machine Learning peut impacter comment l'humain apprend la tâche et construit un modèle mental lui permettant de prévoir le comportement du système.Les objectifs de cette thèse s'articulent autour de deux axes principaux:(i) élaborer une stratégie pour structurer l'apprentissage de tâches motrices (ii)) étudier les systèmes interactifs capables de s'adapter à, mais aussi d'être instruits par l'apprenant, pour optimiser les performances motrices. À travers deux études, nous explorons différentes stratégies d'élaboration de programmes d'apprentissage pour effectuer des tâches motrices. Dans la première étude, nous évaluons la précision et la fluidité du mouvement lors de l'exécution d'une tâche visuo-motrice. La seconde étude explore le contrôle de prothèse fondé sur la technique de reconnaissance de formes. Elle évalue à la fois la précision de reconnaissance des gestes par un algorithme de Machine Learning et la compréhension du système par les participants.Nos résultats contribuent aux travaux dans le domaine de l'apprentissage adaptatif des habiletés motrices ainsi qu'à l'interaction humain-machine. Ils démontrent qu'adapter les tâches motrices à l'apprenant présente des avantages en termes de performance des participants et de leur compréhension du système. Ces résultats offrent des perspectives pour créer des protocoles d'entraînement et faciliter leur implémentation dans un contexte appliquéMotor skill acquisition is the process by which someone is able to perform a movement more accurately. In this context, practice plays a crucial role. However, practice is not always adapted to each learner's needs and learning journey. Generating personalised instructions manually is time-consuming and therefore impractical. Creating personalized practice sessions automatically is one way to alleviate this problem. Adaptive strategies that structure training, i.e. , the sequence of tasks executed, according to task difficulty and skill level have the potential to improve motor learning for the individual. This dual process of a machine learning the sequence adapted to a human learner and the human learning from the machine, is what we call co-learning. In this thesis, we study human-machine co-learning in the context of motor learning, i.e., learning sequences are generated at the same time as the human learns to perform the motor task.Machine learning algorithms can analyze the learning tendencies of individual learners and adapt training instructions accordingly. They can also be used to control a human-machine interface, during which humans learn to adapt their movements (e.g. prosthesis control). In this thesis, we leverage Machine Learning to facilitate the acquisition of motor skills. However, the use of Machine Learning to achieve this goal involves challenges : (i) few data is available to train the algorithms, (ii) the interactive nature of the system requires rapid training of machine learning algorithms. (iii) the effectiveness of the algorithms depends on a precise assessment of the learner's skill level, which is difficult to measure in practice and (iv) the degree of control provided to humans when training the machine learning model can impact their learning and the way they build a mental model to predict the system's behavior.The aims of this thesis are twofold: (i) to develop a strategy for structuring the learning of motor tasks (ii) to study interactive systems that can adapt to and be adapted by the learner to provide guidance during practice. Through two studies, we explore different strategies to sequence motor learning tasks. In the first study, we evaluate the accuracy and smoothness of movement execution during the performance of a visuo-motor task. The second study explores how to train a machine learning algorithm in a prosthesis control task. We evaluate both the recognition accuracy of gestures provided by participants as well as participants' understanding of the system.Our results contribute to the field of adaptive learning of motor skills and Human-Computer Interaction. They demonstrate that adapting motor tasks to the learner has advantages in terms of participants' performance and understanding of the task. These results provide insights for creating training protocols and facilitating their transition to applied contexts
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Crochepierre, Laure. "Apprentissage automatique interactif pour les opérateurs du réseau électrique." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0112.
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Dans le contexte de la transition énergétique et de l'augmentation des interconnexions entre les réseaux de transport d'électricité en Europe, les opérateurs du réseau français doivent désormais faire face à davantage de fluctuations et des dynamiques nouvelles sur le réseau. Pour garantir la sûreté de ce réseau, les opérateurs s'appuient sur des logiciels informatiques permettant de réaliser des simulations, ou de suivre l'évolution d'indicateurs créés manuellement par des experts grâce à leur connaissance du fonctionnement du réseau. Le gestionnaire de réseau de transport d'électricité français RTE (Réseau de Transport d'Electricité) s'intéresse notamment aux développements d'outils permettant d'assister les opérateurs dans leur tâche de surveillance des transits sur les lignes électriques. Les transits sont en effet des grandeurs particulièrement importantes pour maintenir le réseau dans un état de sécurité, garantissant la sûreté du matériel et des personnes. Cependant, les indicateurs utilisés ne sont pas faciles à mettre à jour du fait de l'expertise nécessaire pour les construire et les analyser. Pour répondre à la problématique énoncée, cette thèse a pour objet la construction d'indicateurs, sous la forme d'expressions symboliques, permettant d'estimer les transits sur les lignes électriques. Le problème est étudié sous l'angle de la Régression Symbolique et investigué à la fois par des approches génétiques d'Evolution Grammaticale et d'Apprentissage par Renforcement dans lesquelles la connaissance experte, explicite et implicite, est prise en compte. Les connaissances explicites sur la physique et l'expertise du domaine électrique sont représentées sous la forme d'une grammaire non-contextuelle délimitant l'espace fonctionnel à partir duquel l'expression est créée. Une première approche d'Evolution Grammaticale Interactive propose d’améliorer incrémentalement les expressions trouvées par la mise à jour d'une grammaire entre les apprentissages évolutionnaires. Les expressions obtenues sur des données réelles issues de l'historique du réseau sont validées par une évaluation de métriques d'apprentissages, complétée par une évaluation de leur interprétabilité. Dans un second temps, nous proposons une approche par renforcement pour chercher dans un espace délimité par une grammaire non-contextuelle afin de construire une expression symbolique pertinente pour des applications comportant des contraintes physiques. Cette méthode est validée sur des données de l'état de l'art de la régression symbolique, ainsi qu’un jeu de données comportant des contraintes physiques pour en évaluer l'interprétabilité. De plus, afin de tirer parti des complémentarités entre les capacités des algorithmes d'apprentissage automatique et de l'expertise des opérateurs du réseau, des algorithmes interactifs de Régression Symbolique sont proposés et intégrés dans des plateformes interactives. L'interactivité est employée à la fois pour mettre à jour la connaissance représentée sous forme grammaticale, analyser, interagir avec et commenter les solutions proposées par les différentes approches. Ces algorithmes et interfaces interactifs ont également pour but de prendre en compte de la connaissance implicite, plus difficile à formaliser, grâce à l'utilisation de mécanismes d'interactions basés sur des suggestions et des préférences de l’utilisateurIn the energy transition context and the increase in interconnections between the electricity transmission networks in Europe, the French network operators must now deal with more fluctuations and new network dynamics. To guarantee the safety of the network, operators rely on computer software that allows them to carry out simulations or to monitor the evolution of indicators created manually by experts, thanks to their knowledge of the operation of the network. The French electricity transmission network operator RTE (Réseau de Transport d'Electricité) is particularly interested in developing tools to assist operators in monitoring flows on power lines. Flows are notably important to maintain the network in a safe state, guaranteeing the safety of equipment and people. However, the indicators used are not easy to update because of the expertise required to construct and analyze them.In order to address the stated problem, this thesis aims at constructing indicators, in the form of symbolic expressions, to estimate flows on power lines. The problem is studied from the Symbolic Regression perspective and investigated using both Grammatical Evolution and Reinforcement Learning approaches in which explicit and implicit expert knowledge is taken into account. Explicit knowledge about the physics and expertise of the electrical domain is represented in the form of a Context-Free Grammar to limit the functional space from which an expression is created. A first approach of Interactive Grammatical Evolution proposes to incrementally improve found expressions by updating a grammar between evolutionary learnings. Expressions are obtained on real-world data from the network history, validated by an analysis of learning metrics and an interpretability evaluation. Secondly, we propose a reinforcement approach to search in a space delimited by a Context-Free Grammar in order to build a relevant symbolic expression to applications involving physical constraints. This method is validated on state-of-the-art Symbolic Regression benchmarks and also on a dataset with physical constraints to assess its interpretability.Furthermore, in order to take advantage of the complementarities between the capacities of machine learning algorithms and the expertise of network operators, interactive Symbolic Regression algorithms are proposed and integrated into interactive platforms. Interactivity allows updating the knowledge represented in grammatical form and analyzing, interacting with, and commenting on the solutions found by the different approaches. These algorithms and interactive interfaces also aim to take into account implicit knowledge, which is more difficult to formalize, through interaction mechanisms based on suggestions and user preferences
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Lai, Hien Phuong. "Vers un système interactif de structuration des index pour une recherche par le contenu dans des grandes bases d'images." Phd thesis, Université de La Rochelle, 2013. http://tel.archives-ouvertes.fr/tel-00934842.
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Cette thèse s'inscrit dans la problématique de l'indexation et la recherche d'images par le contenu dans des bases d'images volumineuses. Les systèmes traditionnels de recherche d'images par le contenu se composent généralement de trois étapes: l'indexation, la structuration et la recherche. Dans le cadre de cette thèse, nous nous intéressons plus particulièrement à l'étape de structuration qui vise à organiser, dans une structure de données, les signatures visuelles des images extraites dans la phase d'indexation afin de faciliter, d'accélérer et d'améliorer les résultats de la recherche ultérieure. A la place des méthodes traditionnelles de structuration, nous étudions les méthodes de regroupement des données (clustering) qui ont pour but d'organiser les signatures en groupes d'objets homogènes (clusters), sans aucune contrainte sur la taille des clusters, en se basant sur la similarité entre eux. Afin de combler le fossé sémantique entre les concepts de haut niveau sémantique exprimés par l'utilisateur et les signatures de bas niveau sémantique extraites automatiquement dans la phase d'indexation, nous proposons d'impliquer l'utilisateur dans la phase de clustering pour qu'il puisse interagir avec le système afin d'améliorer les résultats du clustering, et donc améliorer les résultats de la recherche ultérieure. En vue d'impliquer l'utilisateur dans la phase de clustering, nous proposons un nouveau modèle de clustering semi-supervisé interactif en utilisant les contraintes par paires (must-link et cannot-link) entre les groupes d'images. Tout d'abord, les images sont regroupées par le clustering non supervisé BIRCH (Zhang et al., 1996). Ensuite, l'utilisateur est impliqué dans la boucle d'interaction afin d'aider le clustering. Pour chaque itération interactive, l'utilisateur visualise les résultats de clustering et fournit des retours au système via notre interface interactive. Par des simples cliques, l'utilisateur peut spécifier les images positives ainsi que les images négatives pour chaque cluster. Il peut aussi glisser les images entre les clusters pour demander de changer l'affectation aux clusters des images. Les contraintes par paires sont ensuite déduites en se basant sur les retours de l'utilisateur ainsi que les informations de voisinage. En tenant compte de ces contraintes, le système réorganise les clusters en utilisant la méthode de clustering semi-supervisé proposée dans cette thèse. La boucle d'interaction peut être répétée jusqu'à ce que le résultat du clustering satisfasse l'utilisateur. Différentes stratégies pour déduire les contraintes par paires entre les images sont proposées. Ces stratégies sont analysées théoriquement et expérimentalement. Afin d'éviter que les résultats expérimentaux dépendent subjectivement de l'utilisateur humain, un agent logiciel simulant le comportement de l'utilisateur humain pour donner des retours est utilisé pour nos expérimentations. En comparant notre méthode avec la méthode de clustering semi-supervisé la plus populaire HMRF-kmeans (Basu et al., 2004), notre méthode donne de meilleurs résultats.
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Pace, Aaron J. "Guided Interactive Machine Learning." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1355.pdf.
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Krishna, Sooraj. "Modelling communicative behaviours for different roles of pedagogical agents." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS286.
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Les agents dans un environnement d'apprentissage peuvent avoir divers rôles et comportements sociaux qui peuvent influencer les objectifs et la motivation des apprenants de différentes manières. L'apprentissage autorégulé (SRL) est un cadre conceptuel complet qui englobe les aspects cognitifs, métacognitifs, comportementaux, motivationnels et affectifs de l'apprentissage et implique les processus de définition d'objectifs, de suivi des progrès, d'analyse des commentaires, d'ajustement des objectifs et des actions de l'apprenant. Dans cette thèse, nous présentons une interaction d'apprentissage multi-agent impliquant divers rôles d'agent pédagogique visant à améliorer l'autorégulation de l'apprenant tout en s'engageant dans une activité d'apprentissage socialement partagée. Nous avons utilisé des rôles distincts d'agents, définis par leurs attitudes sociales et leurs compétences, pour proposer des stratégies d'échafaudage de régulation spécifiques à l'apprenant. La méthodologie suivie dans cette thèse a commencé par la définition de rôles d'agent pédagogique dans un contexte de régulation socialement partagé et le développement d'une tâche d'apprentissage collaboratif pour faciliter l'autorégulation. Une série d'études d'utilisateurs a été menée pour comprendre les perceptions des apprenants sur les rôles des agents, les comportements associés et la tâche d'apprentissage. Dans l'ensemble, les travaux présentés dans cette thèse explorent comment divers rôles d'agents peuvent être utilisés pour fournir un échafaudage de régulation aux apprenants dans un contexte d'apprentissage socialement partagéAgents in a learning environment can have various roles and social behaviours that can influence the goals and motivation of the learners in distinct ways. Self-regulated learning (SRL) is a comprehensive conceptual framework that encapsulates the cognitive, metacognitive, behavioural, motivational and affective aspects of learning and entails the processes of goal setting, monitoring progress, analyzing feedback, adjustment of goals and actions by the learner. In this thesis, we present a multi-agent learning interaction involving various pedagogical agent roles aiming to improve the self-regulation of the learner while engaging in a socially shared learning activity. We used distinct roles of agents, defined by their social attitudes and competence characteristics, to deliver specific regulation scaffolding strategies for the learner. The methodology followed in this Thesis started with the definition of pedagogical agent roles in a socially shared regulation context and the development of a collaborative learning task to facilitate self-regulation. Based on the learning task framework, we proposed a shared learning interaction consisting of a tutor agent providing external regulation support focusing on the performance of the learner and a peer agent demonstrating co-regulation strategies to promote self-regulation in the learner. A series of user studies have been conducted to understand the learner perceptions about the agent roles, related behaviours and the learning task. Altogether, the work presented in this thesis explores how various roles of agents can be utilised in providing regulation scaffolding to the learners in a socially shared learning context
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Schild, Erwan. "De l’importance de valoriser l’expertise humaine dans l’annotation : application à la modélisation de textes en intentions à l’aide d’un clustering interactif." Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0024.
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La tâche d'annotation, nécessaire à l'entraînement d'assistants conversationnels, fait habituellement appel à des experts du domaine à modéliser. Toutefois, l'annotation de données est connue pour être une tâche difficile en raison de sa complexité et sa subjectivité : elle nécessite par conséquent de solides compétences analytiques dans le but de modéliser les textes en intention de dialogue. De ce fait, la plupart des projets d'annotation choisissent de former les experts aux tâches d'analyse pour en faire des "super-experts". Dans cette thèse, nous avons plutôt décidé mettre l'accent sur les connaissances réelles des experts en proposant une nouvelle méthode d'annotation basée sur un Clustering Interactif. Celle-ci se base sur une coopération Homme/Machine, où la machine réalise un clustering pour proposer une base initiale d'apprentissage, et où l'expert annote des contraintes MUST-LINK ou CANNOT-LINK entre les données pour affiner itérativement la base d'apprentissage proposée. Une telle annotation présente l'avantage d'être plus instinctive, car les experts peuvent associer ou différencier les données en fonction de la similarité de leur cas d'usage, permettant ainsi de traiter les données comme ils le feraient professionnellement au quotidien. Au cours de nos études, nous avons pu montrer que cette méthode diminuait sensiblement la complexité de conception d'une base d'apprentissage, réduisant notamment la nécessité de formation des experts intervenant dans un projet d'annotation. Nous proposons une implémentation technique de cette méthode (algorithmes et interface graphique associée), ainsi qu'une étude des paramètres optimaux pour obtenir une base d'apprentissage cohérente en un minimum d'annotation. Nous réalisons également une étude de coûts (techniques et humains) permettant de confirmer que l'utilisation d'une telle méthode est réaliste dans un cadre industriel. De plus, afin que la méthode atteigne son plein potentiel, nous fournissons un ensemble de conseils, notamment : (1) des recommandations visant à cadrer la stratégie d'annotation, (2) une aide à l'identification et à la résolution des divergences d'opinion entre annotateurs, (3) des indicateurs de rentabilité pour chaque intervention de l'expert, et (4) des méthodes d'analyse de la pertinence de la base d'apprentissage en cours de construction. En conclusion, cette thèse offre une approche innovante pour concevoir une base d'apprentissage d'un assistant conversationnel, permettant d'impliquer les experts du domaine métier pour leurs vraies connaissances, tout en leur demandant un minimum de compétences analytiques et techniques. Ces travaux ouvrent ainsi la voie à des méthodes plus accessibles pour la construction de ces assistantsUsually, the task of annotation, used to train conversational assistants, relies on domain experts who understand the subject matter to model. However, data annotation is known to be a challenging task due to its complexity and subjectivity. Therefore, it requires strong analytical skills to model the text in dialogue intention. As a result, most annotation projects choose to train experts in analytical tasks to turn them into "super-experts". In this thesis, we decided instead to focus on the real knowledge of experts by proposing a new annotation method based on Interactive Clustering. This method involves a Human-Machine cooperation, where the machine performs clustering to provide an initial learning base, and the expert annotates MUST-LINK or CANNOT-LINK constraints between the data to iteratively refine the proposed learning base. Such annotation has the advantage of being more instinctive, as experts can associate or differentiate data according to the similarity of their use cases, allowing them to handle the data as they would professionally do on a daily basis. During our studies, we have been able to show that this method significantly reduces the complexity of designing a learning base, notably by reducing the need for training the experts involved in an annotation project. We provide a technical implementation of this method (algorithms and associated graphical interface), as well as a study of optimal parameters to achieve a coherent learning base with minimal annotation. We have also conducted a cost study (both technical and human) to confirm that the use of such a method is realistic in an industrial context. Finally, we provide a set of recommendations to help this method reach its full potential, including: (1) advice aimed at framing the annotation strategy, (2) assistance in identifying and resolving differences of opinion between annotators, (3) rentability indicators for each expert intervention, and (4) methods for analyzing the relevance of the learning base under construction. In conclusion, this thesis provides an innovative approach to design a learning base for a conversational assistant, involving domain experts for their actual knowledge, while requiring a minimum of analytical and technical skills. This work opens the way for more accessible methods for building such assistants
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Georgiev, Nikolay. "Assisting physiotherapists by designing a system utilising Interactive Machine Learning." Thesis, Uppsala universitet, Institutionen för informatik och media, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447489.
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Millions of people throughout the world suffer from physical injuries and impairments and require physiotherapy to successfully recover. There are numerous obstacles in the way of having access to the necessary care – high costs, shortage of medical personnel and the need to travel to the appropriate medical facilities, something even more challenging during the Covid-19 pandemic. One approach to addressing this issue is to incorporate technology in the practice of physiotherapists, allowing them to help more patients. Using research through design, this thesis explores how interactive machine learning can be utilised in a system, designed for aiding physiotherapists. To this end, after a literature review, an informal case study was conducted. In order to explore what functionality the suggested system would need, an interface prototype was iteratively developed and subsequently evaluated through formative testing by three physiotherapists. All participants found value in the proposed system, and were interested in how such a system can be implemented and potentially used in practice. In particular the ability of the system to monitor the correct execution of the exercises by the patient, and the increased engagement during rehabilitative training brought by the sonification. Several suggestions for future developments in the topic are also presented at the end of this work.
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Kim, Been. "Interactive and interpretable machine learning models for human machine collaboration." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98680.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 135-143).
I envision a system that enables successful collaborations between humans and machine learning models by harnessing the relative strength to accomplish what neither can do alone. Machine learning techniques and humans have skills that complement each other - machine learning techniques are good at computation on data at the lowest level of granularity, whereas people are better at abstracting knowledge from their experience, and transferring the knowledge across domains. The goal of this thesis is to develop a framework for human-in-the-loop machine learning that enables people to interact effectively with machine learning models to make better decisions, without requiring in-depth knowledge about machine learning techniques. Many of us interact with machine learning systems everyday. Systems that mine data for product recommendations, for example, are ubiquitous. However these systems compute their output without end-user involvement, and there are typically no life or death consequences in the case the machine learning result is not acceptable to the user. In contrast, domains where decisions can have serious consequences (e.g., emergency response panning, medical decision-making), require the incorporation of human experts' domain knowledge. These systems also must be transparent to earn experts' trust and be adopted in their workflow. The challenge addressed in this thesis is that traditional machine learning systems are not designed to extract domain experts' knowledge from natural workflow, or to provide pathways for the human domain expert to directly interact with the algorithm to interject their knowledge or to better understand the system output. For machine learning systems to make a real-world impact in these important domains, these systems must be able to communicate with highly skilled human experts to leverage their judgment and expertise, and share useful information or patterns from the data. In this thesis, I bridge this gap by building human-in-the-loop machine learning models and systems that compute and communicate machine learning results in ways that are compatible with the human decision-making process, and that can readily incorporate human experts' domain knowledge. I start by building a machine learning model that infers human teams' planning decisions from the structured form of natural language of team meetings. I show that the model can infer a human teams' final plan with 86% accuracy on average. I then design an interpretable machine learning model then "makes sense to humans" by exploring and communicating patterns and structure in data to support human decision-making. Through human subject experiments, I show that this interpretable machine learning model offers statistically significant quantitative improvements in interpretability while preserving clustering performance. Finally, I design a machine learning model that supports transparent interaction with humans without requiring that a user has expert knowledge of machine learning technique. I build a human-in-the-loop machine learning system that incorporates human feedback and communicates its internal states to humans, using an intuitive medium for interaction with the machine learning model. I demonstrate the application of this model for an educational domain in which teachers cluster programming assignments to streamline the grading process.
by Been Kim.
Ph. D.
Books on the topic "Machine learning interactif"
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Renals, Steve, Samy Bengio, and Jonathan G. Fiscus, eds. Machine Learning for Multimodal Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11965152.
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Popescu-Belis, Andrei, Steve Renals, and Hervé Bourlard, eds. Machine Learning for Multimodal Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78155-4.
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Bengio, Samy, and Hervé Bourlard, eds. Machine Learning for Multimodal Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b105752.
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Popescu-Belis, Andrei, and Rainer Stiefelhagen, eds. Machine Learning for Multimodal Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85853-9.
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Renals, Steve, and Samy Bengio, eds. Machine Learning for Multimodal Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11677482.
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Raedt, Luc de. Interactive theory revision: An inductive logic programming approach. London: Academic Press, 1992.
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Naidenova, Xenia. Machine learning methods for commonsense reasoning processes: Interactive models. Hershey, PA: Information Science Reference, 2010.
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Naidenova, Xenia. Machine learning methods for commonsense reasoning processes: Interactive models. Hershey, PA: Information Science Reference, 2010.
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Bösser, Tom. Learning in man-computer interaction: Areview of the literature. Berlin: Springer-Verlag, 1987.
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Bösser, Tom. Learning in man-computer interaction: A review of the literature. Berlin: Springer-Verlag, 1987.
Find full textBook chapters on the topic "Machine learning interactif"
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Wall, Emily, Soroush Ghorashi, and Gonzalo Ramos. "Using Expert Patterns in Assisted Interactive Machine Learning: A Study in Machine Teaching." In Human-Computer Interaction – INTERACT 2019, 578–99. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29387-1_34.
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Drucker, Steven M., Danyel Fisher, and Sumit Basu. "Helping Users Sort Faster with Adaptive Machine Learning Recommendations." In Human-Computer Interaction – INTERACT 2011, 187–203. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23765-2_13.
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Desolda, Giuseppe, Andrea Esposito, Rosa Lanzilotti, and Maria F. Costabile. "Detecting Emotions Through Machine Learning for Automatic UX Evaluation." In Human-Computer Interaction – INTERACT 2021, 270–79. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85613-7_19.
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Lange, Marvin, Reuben Kirkham, and Benjamin Tannert. "Strategically Using Applied Machine Learning for Accessibility Documentation in the Built Environment." In Human-Computer Interaction – INTERACT 2021, 426–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85616-8_25.
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Kühlwein, Daniel, Jasmin Christian Blanchette, Cezary Kaliszyk, and Josef Urban. "MaSh: Machine Learning for Sledgehammer." In Interactive Theorem Proving, 35–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39634-2_6.
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Raber, Frederic, Felix Kosmalla, and Antonio Krueger. "Fine-Grained Privacy Setting Prediction Using a Privacy Attitude Questionnaire and Machine Learning." In Human-Computer Interaction – INTERACT 2017, 445–49. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68059-0_48.
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Schrammel, Johann. "Exploring New Ways of Utilizing Automated Clustering and Machine Learning Techniques in Information Visualization." In Human-Computer Interaction – INTERACT 2011, 394–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23768-3_41.
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Amunategui, Manuel, and Mehdi Roopaei. "Interactive Drawing Canvas and Digit Predictions Using TensorFlow on GCP." In Monetizing Machine Learning, 263–88. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3873-8_8.
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Kaiser, Michael, Volker Klingspor, and Holger Friedrich. "Human-Agent Interaction and Machine Learning." In Machine Learning: ECML-97, 345–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-62858-4_98.
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Boukhelifa, Nadia, Anastasia Bezerianos, and Evelyne Lutton. "Evaluation of Interactive Machine Learning Systems." In Human and Machine Learning, 341–60. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90403-0_17.
Full textConference papers on the topic "Machine learning interactif"
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Kopiler, Alberto, Tiago Novello, Guilherme Schardong, Luiz Schirmer, Daniel Perazzo, and Luiz Velho. "INTERACT-NET: An Interactive Interface for Multimedia Machine Learning." In 2024 37th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/sibgrapi62404.2024.10716312.
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Saranya, V. S., U. Ganesh Naidu, ParvathananiRajendra Kumar, E. Elamathi, JayavarapuKarthik, and AjithSundaram. "LookCursorAI: Machine Learning-Enhanced Eye-Powered Interaction." In 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC), 606–10. IEEE, 2024. http://dx.doi.org/10.1109/aic61668.2024.10730891.
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Fails, Jerry Alan, and Dan R. Olsen. "Interactive machine learning." In the 8th international conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/604045.604056.
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Plant, Nicola, Ruth Gibson, Carlos Gonzalez Diaz, Bruno Martelli, Michael Zbyszyński, Rebecca Fiebrink, Marco Gillies, Clarice Hilton, and Phoenix Perry. "Movement interaction design for immersive media using interactive machine learning." In MOCO '20: 7th International Conference on Movement and Computing. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3401956.3404252.
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Plant, Nicola, Clarice Hilton, Marco Gillies, Rebecca Fiebrink, Phoenix Perry, Carlos González Díaz, Ruth Gibson, Bruno Martelli, and Michael Zbyszynski. "Interactive Machine Learning for Embodied Interaction Design: A tool and methodology." In TEI '21: Fifteenth International Conference on Tangible, Embedded, and Embodied Interaction. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3430524.3442703.
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Senft, Emmanuel, Séverin Lemaignan, Paul E. Baxter, and Tony Belpaeme. "Leveraging Human Inputs in Interactive Machine Learning for Human Robot Interaction." In HRI '17: ACM/IEEE International Conference on Human-Robot Interaction. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3029798.3038385.
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Teso, Stefano, and Kristian Kersting. "Explanatory Interactive Machine Learning." In AIES '19: AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3306618.3314293.
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Goel, Shivam. "Teaching Robots to Interact with Humans in a Smart Environment." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/906.
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Robotics in healthcare has recently emerged, backed by the recent advances in the field of machine learning and robotics. Researchers are focusing on training robots for interacting with elderly adults. This research primarily focuses on engineering more efficient robots that can learn from their mistakes, thereby aiding in better human-robot interaction. In this work, we propose a method in which a robot learns to navigate itself to the individual in need. The robotic agents' learning algorithm will be capable of navigating in an unknown environment. The robot's primary objective is to locate human in a house, and upon finding the human, the goal is to interact with them while complementing their pose and gaze. We propose an end to end learning strategy, which uses a recurrent neural network architecture in combination with Q-learning to train an optimal policy. The idea can be a contribution to better human-robot interaction.
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Gorokhovatskyi, Oleksii, Nataliya Vnukova, Viktoriia Ostapenko, and Viktoriia Tyschenko. "Semantic-based Clustering for Education-Science-Business Interaction Bibliometric Analysis." In Machine Learning Workshop at CoLInS 2024. CoLInS, 2024. http://dx.doi.org/10.31110/colins/2024-1/010.
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Dan, Dorin, and Mariana Ursache. "INTERACTIVE LABORATORY FOR COMPUTER-ASSISTED STUDY OF WARP KNITTING MACHINES." In eLSE 2013. Carol I National Defence University Publishing House, 2013. http://dx.doi.org/10.12753/2066-026x-13-281.
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The paper presents a laboratory module that contains an interactive graphical software package for the study of the warp knitting machines. The package includes the following modules: an interactive photo album which is used to demonstrate the destination of warp knitting machines, an interactive module for the presentation and description of the working knitting elements in case of a warp knitting machine, animations that present correlated movements of working knitting elements caught in a cycle for the three types of needles (spring bearded, latch and compound) and interactive presentation of a warp knitting machine in laboratory assemblies highlighting the main mechanisms of it. AVI movies that show a knitting machine in running order is also used. Interactive graphic programs were carried out using CorelDraw and Corel RAVE application and saved as flash files. Laboratory module was designed based on an XML file taken as a model, amended contents needs of the laboratory and then used for transformation in the HTML laboratory module using ModulEst application. Laboratory module has an interactive graphical structure and contains navigation icons for direct access to the bibliography, webography, glossary, summary help page and test for self-programming and verification. Learning modules are designed as additional learning material and is based on student-centered education principle. The module can be used in knitting laboratory work. Laboratory module can be accessed online or can be used offline. The e-learning interactive tools presented in this paper represents the outcome of the process of innovating the working methods applied in the knitting laboratory of the Faculty of Textiles, Leather and Industrial Management of Iasi.
Reports on the topic "Machine learning interactif"
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Porter, Reid B., James P. Theiler, and Donald R. Hush. Interactive Machine Learning in Data Exploitation. Office of Scientific and Technical Information (OSTI), January 2013. http://dx.doi.org/10.2172/1060903.
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Suter, Jonathan, Johnathan Cree, Jesse Johns, and Gianluca Longoni. Neural Interactive Machine Learning: Final Report: Compilation of presentation material. Office of Scientific and Technical Information (OSTI), June 2021. http://dx.doi.org/10.2172/1988291.
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Shukla, Indu, Rajeev Agrawal, Kelly Ervin, and Jonathan Boone. AI on digital twin of facility captured by reality scans. Engineer Research and Development Center (U.S.), November 2023. http://dx.doi.org/10.21079/11681/47850.
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The power of artificial intelligence (AI) coupled with optimization algorithms can be linked to data-rich digital twin models to perform predictive analysis to make better informed decisions about installation operations and quality of life for the warfighters. In the current research, we developed AI connected lifecycle building information models through the creation of a data informed smart digital twin of one of US Army Corps of Engineers (USACE) buildings as our test case. Digital twin (DT) technology involves creating a virtual representation of a physical entity. Digital twin is created by digitalizing data collected through sensors, powered by machine learning (ML) algorithms, and are continuously learning systems. The exponential advance in digital technologies enables facility spaces to be fully and richly modeled in three dimensions and can be brought together in virtual space. Coupled with advancement in reinforcement learning and computer graphics enables AI agents to learn visual navigation and interaction with objects. We have used Habitat AI 2.0 to train an embodied agent in immersive 3D photorealistic environment. The embodied agent interacts with a 3D environment by receiving RGB, depth and semantically segmented views of the environment and taking navigational actions and interacts with the objects in the 3D space. Instead of training the robots in physical world we are training embodied agents in simulated 3D space. While humans are superior at critical thinking, creativity, and managing people, whereas robots are superior at coping with harsh environments and performing highly repetitive work. Training robots in controlled simulated world is faster and can increase their surveillance, reliability, efficiency, and survivability in physical space.
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Bao, Jieyi, Xiaoqiang Hu, Cheng Peng, Junyi Duan, Yizhou Lin, Chengcheng Tao, Yi Jiang, and Shuo Li. Advancing INDOT’s Friction Test Program for Seamless Coverage of System: Pavement Markings, Typical Aggregates, Color Surface Treatment, and Horizontal Curves. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317734.
Full textAbstract:
Various highway projects, roadway safety, and maintenance all hinge on pavement friction. INDOT's pavement friction test program has played a crucial role in addressing issues like wet pavement crash reduction, durable pavements surface friction, and sustainable aggregates. However, changes in the transportation sector, allied industries, societal needs, and economics present unique challenges that require proactive solutions. First, the existing field friction testing method, which uses a locked wheel skid tester (LWST) is limited to straight, flat pavement sections and excludes crash-prone areas like horizontal curves. Upgrading the program to cover horizontal curves on two-lane rural highways is vital for road safety. Second, the demand for friction testing on pavement markings at crash sites is rising. There's currently no widely accepted standard method for national-scale pavement marking friction testing. The shift to wider longitudinal pavement markings, from 4" to 6", driven by both human and autonomous vehicle safety, presents challenges for motorcyclists and pedestrians. The third challenge focuses on Color Surface Treatment (CST), which is increasingly used in Indiana bus and bike lanes for visibility, lane discipline, and friction performance, especially under frequent bus acceleration and braking. However, a lack of laboratory and field data necessitates investigating CST's metrics and requirements for adequate friction. Advancing INDOT's friction testing program to cover the entire highway system and address emerging friction challenges is imperative. The goals of this study included enhancing INDOT's friction testing, ensuring comprehensive highway network coverage and providing reliable friction data to help INDOT address safety concerns. The research encompassed a thorough evaluation of various aggregates and pavement marking materials commonly used in Indiana through laboratory experiments, field tests, and data analysis to unveil their influence on pavement friction. Field friction measurements on colored bus and bike lanes were also conducted and thoroughly analyzed. Moreover, the tire-pavement interaction on horizontal curves was assessed on airport runways and highway sections through mechanistic-empirical analysis, and a friction testing model for horizontal curves was devised using finite element analysis and machine learning methodologies.