On Task-specific Autonomy in Robotic Interventions: A Multimodal Learning-based Approach for Multi-level Skill Assessment during Cyborg Catheterization

Lack of learning-based methods for characterizing the multimodal data generated during cyborg catheterization hinders the drive towards autonomous robotic control. Also, multiplexing salient features from multiple data-sources can enhance effective assessment and classification of domain skills for apt intelligent surgeon-robot (cyborg) catheterization during intravascular interventions. In this study, task-specific autonomous intervention is envisioned upon an isomorphic master-slave robotic catheter system that exhibit hand defter techniques used in Cath Labs. To drive cyborg catheterization, stacking-based deep neural network is developed for three-level skill assessment.<br>

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A Basic Study on the Flow and Classification of Multi-level Urban Planning

International Journal of ICT-aided Architecture and Civil Engineering ◽

10.21742/ijiace.2017.4.2.06 ◽

2017 ◽

Vol 4 (2) ◽

Author(s):

Surin Kim ◽

Keyword(s):

Urban Planning ◽

Basic Study ◽

Multi Level

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Classification of Evaluation Methods for the Effective Assessment of Simulation Games: Results from a Literature Review

International Journal of Engineering Pedagogy (iJEP) ◽

10.3991/ijep.v9i1.9948 ◽

2019 ◽

Vol 9 (1) ◽

pp. 19 ◽

Cited By ~ 4

Author(s):

Niluefer Deniz Faizan ◽

Alexander Löffler ◽

Robert Heininger ◽

Matthias Utesch ◽

Helmut Krcmar

Keyword(s):

Literature Review ◽

Current Trend ◽

Evaluation Methods ◽

Simulation Game ◽

Simulation Games ◽

Research Papers ◽

Free Environment ◽

Effective Assessment ◽

A Current

As a current trend in teaching, simulation games play an active and important role in the area of technology-based education. Simulation games create an envi-ronment for scholars to solve real-world problems in a risk-free environment. Therefore, they aim to increase the knowledge base as well as learning experienc-es for students. However, assessing the effectiveness of a simulation game is necessary to optimize elements of the game and increase their learning effect. In order to achieve this aim, different evaluation methods exist, which do not always involve all phases when running a simulation game. In this study, we conduct a literature review to analyze evaluation methods for three phases of simulation games: pre-game, in-game, and post-game. Thirty-one peer-reviewed research papers met specified selection criteria and we classified them according to a di-dactic framework that illustrates four phases of running simulation games: Prepa-ration, Introduction, Interaction and Conclusion phase. Based on the results, we provide a concrete evaluation strategy that will be a guide to assess simulation games during all phases. This study contributes to theory by providing an over-view of evaluation methods for the assessment of simulation games within the different game phases. It contributes to practice by providing a concrete evalua-tion strategy that can be adapted and used to assess simulation games.

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Assessment in Inclusive Settings

Handbook of Research on Classroom Diversity and Inclusive Education Practice - Advances in Educational Technologies and Instructional Design ◽

10.4018/978-1-5225-2520-2.ch018 ◽

2017 ◽

pp. 426-443

Author(s):

Brittany L. Hott ◽

Rebecca A. Dibbs ◽

DeMarquis Hayes ◽

Lesli P. Raymond

Keyword(s):

Decision Making ◽

Instructional Decisions ◽

Assessment Practices ◽

Critical Component ◽

Level System ◽

Quality Instruction ◽

Inclusive Settings ◽

Multi Level ◽

Instructional Decision ◽

Effective Assessment

Assessment is one of the most controversial and challenging aspects of education. While increasing emphasis has been placed on student progress and accountability, effective assessment processes are often overlooked as a critical component of quality instruction. This chapter aims to provide practitioners, educators, and policymakers with an overview of assessment practices that provide information at the classroom and individual levels to drive instructional decision making. A multi-level system of support model is emphasized to illustrate types and administration of assessments needed to make instructional decisions.

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Service system well-being: conceptualising a holistic concept

Journal of Service Management ◽

10.1108/josm-01-2019-0036 ◽

2019 ◽

Vol 30 (6) ◽

pp. 766-792 ◽

Cited By ~ 2

Author(s):

Wei Wei Cheryl Leo ◽

Gaurangi Laud ◽

Cindy Yunhsin Chou

Keyword(s):

Service System ◽

Case Study Research ◽

Well Being ◽

Content Type ◽

Holistic Concept ◽

Multi Level ◽

Multi Stakeholder ◽

Service Managers

Purpose The purpose of this paper is to develop a concept of service system well-being by presenting its collective conceptualisation and ten key domains. Design/methodology/approach Service system well-being domains were established using multi-level theory and a qualitative case study research design. To validate the domains initially developed from the literature, 19 in-depth interviews were conducted across two case studies that represented the service systems of a hospital and a multi-store retail franchise chain. A multi-stakeholder approach was used to explore the actor’s perspectives about service system well-being. Key domains of service system well-being were identified using deductive categorisation analysis. Findings The findings found evidence of ten key domains of well-being, namely strategic, governance, leadership, resource, community, social, collaborative, cultural, existential and transformational, among service system stakeholders. Research limitations/implications Service system well-being is a collective concept comprising ten domains that emerged at different levels of the service system. The propositions outlined the classification of and interlinkages between the domains. This exploratory study was conducted in a limited service context and focussed on ten key domains. Practical implications Service managers in commercial and social organisations are able to apply the notion of service system well-being to identify gaps and nurture well-being deficiencies within different domains of service-system well-being. Originality/value Based on multi-level theory, the study is the first to conceptualise and explore the concept of service system well-being across multiple actors.

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Multi-level classification of emphysema in HRCT lung images

Pattern Analysis and Applications ◽

10.1007/s10044-007-0093-7 ◽

2007 ◽

Vol 12 (1) ◽

pp. 9-20 ◽

Cited By ~ 21

Author(s):

Mithun Prasad ◽

Arcot Sowmya ◽

Peter Wilson

Keyword(s):

Multi Level

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Multimodal Data Value Chain (M-DVC): A Conceptual Tool to Support the Development of Multimodal Learning Analytics Solutions

IEEE Revista Iberoamericana de Tecnologias del Aprendizaje ◽

10.1109/rita.2020.2987887 ◽

2020 ◽

Vol 15 (2) ◽

pp. 113-122 ◽

Cited By ~ 2

Author(s):

Shashi Kant Shankar ◽

Maria Jesus Rodriguez-Triana ◽

Adolfo Ruiz-Calleja ◽

Luis P. Prieto ◽

Pankaj Chejara ◽

...

Keyword(s):

Value Chain ◽

Learning Analytics ◽

Multimodal Learning ◽

Conceptual Tool ◽

Multimodal Data ◽

Data Value ◽

Multimodal Learning Analytics

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Found in Translation: Learning Robust Joint Representations by Cyclic Translations between Modalities

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33016892 ◽

2019 ◽

Vol 33 ◽

pp. 6892-6899 ◽

Cited By ~ 10

Author(s):

Hai Pham ◽

Paul Pu Liang ◽

Thomas Manzini ◽

Louis-Philippe Morency ◽

Barnabás Póczos

Keyword(s):

Sentiment Analysis ◽

State Of The Art ◽

Research Area ◽

Test Time ◽

Multimodal Learning ◽

Missing Information ◽

Multimodal Data ◽

Translation Model ◽

Recent Success ◽

Multimodal Sentiment Analysis

Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities. The central challenge in multimodal learning involves inferring joint representations that can process and relate information from these modalities. However, existing work learns joint representations by requiring all modalities as input and as a result, the learned representations may be sensitive to noisy or missing modalities at test time. With the recent success of sequence to sequence (Seq2Seq) models in machine translation, there is an opportunity to explore new ways of learning joint representations that may not require all input modalities at test time. In this paper, we propose a method to learn robust joint representations by translating between modalities. Our method is based on the key insight that translation from a source to a target modality provides a method of learning joint representations using only the source modality as input. We augment modality translations with a cycle consistency loss to ensure that our joint representations retain maximal information from all modalities. Once our translation model is trained with paired multimodal data, we only need data from the source modality at test time for final sentiment prediction. This ensures that our model remains robust from perturbations or missing information in the other modalities. We train our model with a coupled translationprediction objective and it achieves new state-of-the-art results on multimodal sentiment analysis datasets: CMU-MOSI, ICTMMMO, and YouTube. Additional experiments show that our model learns increasingly discriminative joint representations with more input modalities while maintaining robustness to missing or perturbed modalities.

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