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8 results on '"Mukesh Barange"'

1. A novel focus encoding scheme for addressee detection in multiparty interaction using machine learning algorithms

Author

Julien Saunier, Usman Malik, Alexandre Pauchet, Mukesh Barange, Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU), Equipe Multi-agent, Interaction, Décision (MIND - LITIS), Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Université Le Havre Normandie (ULH), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Normandie Université (NU), Institut des Systèmes Intelligents et de Robotique (ISIR), and Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Scheme (programming language), Computer science, Feature selection, 02 engineering and technology, 010501 environmental sciences, Machine learning, computer.software_genre, 01 natural sciences, Task (project management), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0202 electrical engineering, electronic engineering, information engineering, Encoding (semiotics), [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, computer.programming_language, Focus (computing), business.industry, Deep learning, Turn-taking, Human-Computer Interaction, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], Signal Processing, Dyadic interaction, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Algorithm
Abstract

Addressee detection is a fundamental task for seamless dialogue management and turn taking in human-agent interaction. Though addressee detection is implicit in dyadic interaction, it becomes a challenging task when more than two participants are involved. This article proposes multiple addressee detection models based on smart feature selection and focus encoding schemes. The models are trained using different machine learning and deep learning algorithms. This research work improves existing baseline accuracies for addressee prediction on two datasets. In addition, the article explores the impact of different focus encoding schemes in several addressee detection cases. Finally, an implementation strategy for addressee detection model in real-time is discussed.

Published
2021
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2. A Generic Machine Learning based Approach for Addressee Detection in Multiparty Interaction

Author

Julien Saunier, Naser Ghannad, Mukesh Barange, Alexandre Pauchet, Usman Malik, Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), École Nationale d'Ingénieurs de Brest (ENIB), Equipe Multi-agent, Interaction, Décision (MIND - LITIS), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH), Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), and Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)

Subjects
Computer science, business.industry, Human-centered computing → Natural language interfaces, Turn-taking, 02 engineering and technology, 010501 environmental sciences, Dialogue management, Machine learning, computer.software_genre, 01 natural sciences, Multimodal interaction, Task (project management), CCS CONCEPTS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Data set, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], Computing methodologies → Machine learning, Dyadic interaction, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], business, computer, 0105 earth and related environmental sciences
Abstract

International audience; Addressee detection is one of the most fundamental tasks for seamless dialogue management and turn taking in human-agent interaction. Whereas addressee detection is implicit in dyadic interaction, it becomes a challenging task in multiparty interactions when more than two participants are involved. Existing research works employ either rule-based or statistical approaches for addressee detection. However, most of these works either have been tested on a single data set or only support a fixed number of participants. In this article, we propose a model based on generic features to predict the addressee in data sets with varying number of participants. The results tested on two different corpora show that the proposed model outperforms existing baselines.

Published
2019
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3. Using Multimodal Information to Enhance Addressee Detection in Multiparty Interaction

Author

Mukesh Barange, Alexandre Pauchet, Usman Malik, Julien Saunier, Saunier, Julien, Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Equipe Multi-agent, Interaction, Décision (MIND - LITIS), and Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Exploit, business.industry, Computer science, Deep learning, 05 social sciences, 020207 software engineering, Rule-based system, Feature selection, Intelligent Agents, 02 engineering and technology, computer.software_genre, Machine learning, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Task (project management), Human-Computer Interaction, Machine Learning, Intelligent agent, 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, Artificial intelligence, Baseline (configuration management), business, computer, 050107 human factors
Abstract

International audience; Addressee detection is an important challenge to tackle in order to improve dialogical interactions between humans and agents. This detection, essential for turn-taking models, is a hard task in multiparty conditions. Rule based as well as statistical approaches have been explored. Statistical approaches, particularly deep learning approaches, require a huge amount of data to train. However, smart feature selection can help improve addressee detection on small datasets, particularly if multimodal information is available. In this article, we propose a statistical approach based on smart feature selection that exploits contextual and multimodal information for addressee detection. The results show that our model outperforms an existing baseline.

Published
2019
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4. Performance Comparison of Machine Learning Models Trained on Manual vs ASR Transcriptions for Dialogue Act Annotation

Author

Mukesh Barange, Usman Malik, Julien Saunier, Alexandre Pauchet, Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Equipe Multi-agent, Interaction, Décision (MIND - LITIS), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH), and Saunier, Julien

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], InformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g.,HCI), Computer science, 02 engineering and technology, [INFO] Computer Science [cs], Machine learning, computer.software_genre, ComputingMethodologies_ARTIFICIALINTELLIGENCE, Task (project management), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Annotation, Transcription (linguistics), 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], ComputingMilieux_MISCELLANEOUS, 060201 languages & linguistics, business.industry, Statistical model, 06 humanities and the arts, Support vector machine, Bag-of-words model, Test set, 0602 languages and literature, Task analysis, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer
Abstract

Automatic dialogue act annotation of speech utterances is an important task in human-agent interaction in order to correctly interpret user utterances. Speech utterances can be transcribed manually or via Automatic Speech Recognizer (ASR). In this article, several Machine Learning models are trained on manual and ASR transcriptions of user utterances, using bag of words and n-grams feature generation approaches, and evaluated on ASR transcribed test set. Results show that models trained using ASR transcriptions perform better than algorithms trained on manual transcription. The impact of irregular distribution of dialogue acts on the accuracy of statistical models is also investigated, and a partial solution to this issue is shown using multimodal information as input.

Published
2018

5. A methodology for the design of pedagogically adaptable learning environments

Author

Ronan Querrec, Julien Saunier, Bernard Blandin, Mukesh Barange, Buche, Cédric, Centre Européen de Réalité Virtuelle (CERV), and École Nationale d'Ingénieurs de Brest (ENIB)

Subjects
0209 industrial biotechnology, Engineering, 021103 operations research, Multimedia, business.industry, Learning environment, 0211 other engineering and technologies, 02 engineering and technology, [INFO] Computer Science [cs], Virtual reality, UML meta-model, computer.software_genre, Training (civil), Preventive maintenance, Intervention (law), 020901 industrial engineering & automation, pedagogical scenario, learning environment, Digital resources, [INFO]Computer Science [cs], business, computer, Instructional simulation
Abstract

In the last decades, the industry has profoundly integrated the use of digital resources in their production process. However, these assets are rarely re-used for the training of the users, operators and technicians that have to interact with these objects. Furthermore, although training and learning environments are classical applications of virtual reality, the design of these environments is generally ad hoc, i.e. dedicated to specific operations on specific objects, hence requiring the intervention of programmers whenever a modification of the pedagogical scenario is required. In this article, we propose a methodology to design adaptable virtual environments, by separating the role of the different protagonists that play a part in the creation of learning environments. In particular, its goal is to allow the teachers to implement different scenarios according to the level of the trainees and to the pedagogical objectives without the intervention of computer scientists. An example of adaptable wind turbine environment is shown, with three different learning situations: simulator, safety training and preventive maintenance training.

Published
2016
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6. Multiparty Interactions for Coordination in a Mixed Human-Agent Teamwork

Author

Alexandre Pauchet, Julien Saunier, Mukesh Barange, Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Equipe Multi-agent, Interaction, Décision (MIND - LITIS), and Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH)

Subjects
Teamwork, Computer science, media_common.quotation_subject, Context (language use), 0102 computer and information sciences, 02 engineering and technology, Plan (drawing), computer.software_genre, 01 natural sciences, 010201 computation theory & mathematics, Order (exchange), Virtual machine, Human–computer interaction, 0202 electrical engineering, electronic engineering, information engineering, Human agent, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Set (psychology), computer, Human learning, ComputingMilieux_MISCELLANEOUS, media_common
Abstract

Virtual environments for human learning enable one or more users to interact with virtual agents in order to perform their tasks. This collaboration necessitates that the members of the team share a set of beliefs and reason about resources, plans and actions to be implemented. This article introduces a new multiparty coordination model allowing several virtual and human agents to dialogue and reason about the tasks that the user must learn. The proposed model relies on a shared plan based approach to represent the beliefs of the team members. The management of the multiparty aspect makes it possible to differentiate the behaviors to be produced according to the type of receiver of a communication: recipient or listener. Finally, in the context of learning a procedural activity, a study examines the effect of our multiparty model on a learner. Results show that the use of proactive pedagogical agents with multiparty competencies boosts the construction of common beliefs.

Published
2017

7. Designing adaptable virtual reality learning environments

Author

Mukesh Barange, Joanna Taoum, Bernard Blandin, Ronan Querrec, Julien Saunier, Saunier, Julien, Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Université Le Havre Normandie (ULH), Normandie Université (NU), Centre Européen de Réalité Virtuelle (CERV), École Nationale d'Ingénieurs de Brest (ENIB), Lab-STICC_ENIB_CID_IHSEV, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), and Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)

Subjects
Multimedia, Computer science, 05 social sciences, 050301 education, 020207 software engineering, 02 engineering and technology, Virtual reality, [INFO] Computer Science [cs], computer.software_genre, Preventive maintenance, Intervention (law), 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], 0503 education, computer, ComputingMilieux_MISCELLANEOUS, Instructional simulation
Abstract

The EAST (Scientific and technical learning environments) project aims at stimulating the interest of young people for science through virtual reality environments, based on industrial assets. Although training and learning environments are classical applications of virtual reality, the design of these environments is generally ad hoc, hence requiring the intervention of programmers whenever a modification of the pedagogical scenario is required. In this paper, we propose a methodology to design virtual environments which can be adapted by teachers to implement different scenarios according to the level of the trainees and to the pedagogical objectives. Current demonstrators include a windmill with three different learning situations: simulator, safety training and preventive maintenance training.

Published
2016

8. Get Involved in an Interactive Virtual Tour of Brest Harbour: Follow the Guide and Participate

Author

Thanh-Hai Trinh, Ronan Querrec, Julien Soler, Pierre De Loor, Vincent Louis, Eric Maisel, Pierre Chevaillier, Mukesh Barange, École Nationale d'Ingénieurs de Brest (ENIB), Lab-STICC_ENIB_CID_IHSEV, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Recherche et Développement, France Télécom, and ANR-10-CORD-0012,CORVETTE,COllaboRative Virtual Environment Technical Training and Experiment(2010)

Subjects
Virtual tour, Multimedia, Computer science, business.industry, 020207 software engineering, Cultural Heritage, 02 engineering and technology, computer.software_genre, Semantics, World Wide Web, Cultural heritage, Shipbuilding, Virtual machine, Meta level, Dialogue Management, Harbour, 0202 electrical engineering, electronic engineering, information engineering, Semantic Modelling, 020201 artificial intelligence & image processing, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], business, computer, Natural language, computer.programming_language
Abstract

International audience; Recent cultural heritage applications have been based on rich-content virtual environment (VE), in which virtual humans can communicate with visitors and other agents using natural language (NL). The conceptualisation of these dialogues are dependent on the contents of the application. Hence, we propose to use the semantic modelling of the VE and the agents' activities for the conceptualisation of the dialogue. Meta-level semantic information are used as arguments in NLU/NLG rules. The advantage of this approach is that the dialogue rules are independent from the contents of the application and have clear semantics. We applied these principles to develop Brest'Coz, an interactive virtual tour for the learning of shipbuilding techniques used in France in early 18 th century.

Published
2011
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