IEEE Organizations related to Collaborative Intelligence

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Conferences related to Collaborative Intelligence

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2020 IEEE 23rd International Conference on Information Fusion (FUSION)

The International Conference on Information Fusion is the premier forum for interchange of the latest research in data and information fusion, and its impacts on our society. The conference brings together researchers and practitioners from academia and industry to report on the latest scientific and technical advances.


2020 IEEE Frontiers in Education Conference (FIE)

The Frontiers in Education (FIE) Conference is a major international conference focusing on educational innovations and research in engineering and computing education. FIE 2019 continues a long tradition of disseminating results in engineering and computing education. It is an ideal forum for sharing ideas, learning about developments and interacting with colleagues inthese fields.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

HRI is a highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.

  • 2018 13th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchersin robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior,anthropology, and many other fields.

  • 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    The conference serves as the primary annual meeting for researchers in the field of human-robot interaction. The event will include a main papers track and additional sessions for posters, demos, and exhibits. Additionally, the conference program will include a full day of workshops and tutorials running in parallel.

  • 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    This conference focuses on the interaction between humans and robots.

  • 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a single -track, highly selective annual conference that showcases the very bestresearch and thinking in human -robot interaction. HRI is inherently interdisciplinary and multidisciplinary,reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificialintelligence, organizational behavior, anthropology, and many other fields.

  • 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a highly selective annual conference that showcases the very best research and thinking in human -robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.

  • 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a single -track, highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.

  • 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a single-track, highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.

  • 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    Robot companions Lifelike robots Assistive (health & personal care) robotics Remote robots Mixed initiative interaction Multi-modal interaction Long-term interaction with robots Awareness and monitoring of humans Task allocation and coordination Autonomy and trust Robot-team learning User studies of HRI Experiments on HRI collaboration Ethnography and field studies HRI software architectures HRI foundations Metrics for teamwork HRI group dynamics.

  • 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    TOPICS: Robot companions, Lifelike robots, Assistive (health & personal care) robotics, Remote robots, Mixed initiative interaction, Multi-modal interaction, Long-term interaction with robots, Awareness and monitoring of humans, Task allocation and coordination, Autonomy and trust, Robot-team learning, User studies of HRI, Experiments on HRI collaboration, Ethnography and field studies, HRI software architectures

  • 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    * Robot companions * Lifelike robots * Assistive (health & personal care) robotics * Remote robots * Mixed initiative interaction * Multi-modal interaction * Long-term interaction with robots * Awareness and monitoring of humans * Task allocation and coordination * Autonomy and trust * Robot-team learning * User studies of HRI * Experiments on HRI collaboration * Ethnography and field studies * HRI software architectures

  • 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    Robot companions Lifelike robots Assistive (health & personal care) robotics Remote robots Mixed initiative interaction Multi-modal interaction Long-term interaction with robots Awareness and monitoring of humans Task allocation and coordination Autonomy and trust Robot-team learning User studies of HRI Experiments on HRI collaboration Ethnography and field studies HRI software architectures HRI foundations Metrics for teamwork HRI group dynamics Individual vs. group HRI

  • 2007 2nd Annual Conference on Human-Robot Interaction (HRI)


2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

Computer in Technical Systems, Intelligent Systems, Distributed Computing and VisualizationSystems, Communication Systems, Information Systems Security, Digital Economy, Computersin Education, Microelectronics, Electronic Technology, Education


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Periodicals related to Collaborative Intelligence

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Most published Xplore authors for Collaborative Intelligence

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Xplore Articles related to Collaborative Intelligence

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Collaborative Intelligence and Decentralized Business Community Building–Potentials in Food/Nutrition Sector

2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW), 2018

This paper shows the potential of Luxembourg Slovenian Business Club's applied research in Collaborative Intelligence for building of large decentralized business communities in Food/Nutrition sector and enabling the creation of consortia at developing new business models. Although Food/Nutrition sector, and related communities are dispersed in many ways: geography, traditions, living standard, ideology, taste preferences, etc., eating is one of the ...


Near-Lossless Deep Feature Compression for Collaborative Intelligence

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP), 2018

Collaborative intelligence is a new paradigm for efficient deployment of deep neural networks across the mobile-cloud infrastructure. By dividing the network between the mobile and the cloud, it is possible to distribute the computational workload such that the overall energy and/or latency of the system is minimized. However, this necessitates sending deep feature data from the mobile to the cloud ...


Towards Collaborative Intelligence Friendly Architectures for Deep Learning

20th International Symposium on Quality Electronic Design (ISQED), 2019

Modern mobile devices are equipped with highperformance hardware resources such as graphics processing units (GPUs), making the end-side intelligent services more feasible. Even recently, specialized silicons as neural engines are being used for mobile devices. However, most mobile devices are still not capable of performing real-time inference using very deep models. Computations associated with deep models for today's intelligent applications ...


Smart Sensing for HVAC Control: Collaborative Intelligence in Optical and IR Cameras

IEEE Transactions on Industrial Electronics, 2018

Energy management of heating, ventilation, and cooling (HVAC) has become a primary concern for residential and commercial buildings. In order to save energy without compromising the comfort of occupants, various techniques have been explored to sense the real-time occupancy/vacancy of HVAC zones. Among all these approaches, wireless-camera-based sensing stands out for its potential application in surveillance and security in addition ...


Graded Concepts for Collaborative Intelligence

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018

Collaborative intelligence involves a combination of human and machine-based analysis, in which humans focus on higher-level tasks involving insight and understanding, whilst machines deal with gathering, filtering and processing data into a convenient and understandable form. We have proposed the use of graded concept lattices as a representation for exchanging information between machine and human in a collaborative intelligent system. ...


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Educational Resources on Collaborative Intelligence

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IEEE-USA E-Books

  • Collaborative Intelligence and Decentralized Business Community Building–Potentials in Food/Nutrition Sector

    This paper shows the potential of Luxembourg Slovenian Business Club's applied research in Collaborative Intelligence for building of large decentralized business communities in Food/Nutrition sector and enabling the creation of consortia at developing new business models. Although Food/Nutrition sector, and related communities are dispersed in many ways: geography, traditions, living standard, ideology, taste preferences, etc., eating is one of the most universal activities. Global challenges are pointing at Mega-trends which are opening opportunities for decentralized collaboration along the whole Food/Nutrition value chain. LSBC is looking to transfer its experience with Collaboration on a micro level (use case of community building of local small food producers) to decentralized communities on a large scale. LSBC is exploring community building fostering mechanisms, based on Blockchain technology, by applying principles of AI networks, such as self-regulation, self-learning, and developing concept such as stimulating connecting activity of agents/nodes. The main goal of this research is to establish principles of effective information exchange that will enable the creation of communication tools for optimizing connectivity of decentralized communities.

  • Near-Lossless Deep Feature Compression for Collaborative Intelligence

    Collaborative intelligence is a new paradigm for efficient deployment of deep neural networks across the mobile-cloud infrastructure. By dividing the network between the mobile and the cloud, it is possible to distribute the computational workload such that the overall energy and/or latency of the system is minimized. However, this necessitates sending deep feature data from the mobile to the cloud in order to perform inference. In this work, we examine the differences between the deep feature data and natural image data, and propose a simple and effective near-lossless deep feature compressor. The proposed method achieves up to 5% bit rate reduction compared to HEVC-Intra and even more against other popular image codecs. Finally, we suggest an approach for reconstructing the input image from compressed deep features that could serve to supplement the inference performed by the deep model.

  • Towards Collaborative Intelligence Friendly Architectures for Deep Learning

    Modern mobile devices are equipped with highperformance hardware resources such as graphics processing units (GPUs), making the end-side intelligent services more feasible. Even recently, specialized silicons as neural engines are being used for mobile devices. However, most mobile devices are still not capable of performing real-time inference using very deep models. Computations associated with deep models for today's intelligent applications are typically performed solely on the cloud. This cloud-only approach requires significant amounts of raw data to be uploaded to the cloud over the mobile wireless network and imposes considerable computational and communication load on the cloud server. Recent studies have shown that the latency and energy consumption of deep neural networks in mobile applications can be notably reduced by splitting the workload between the mobile device and the cloud. In this approach, referred to as collaborative intelligence, intermediate features computed on the mobile device are offloaded to the cloud instead of the raw input data of the network, reducing the size of the data needed to be sent to the cloud. In this paper, we design a new collaborative intelligence friendly architecture by introducing a unit responsible for reducing the size of the feature data needed to be offloaded to the cloud to a greater extent, where this unit is placed after a selected layer of a deep model. This unit is referred to as the butterfly unit. The butterfly unit consists of the reduction unit and the restoration unit. The outputs of the reduction unit is offloaded to the cloud server on which the computations associated with the restoration unit and the rest of the inference network are performed. Both the reduction and restoration units use a convolutional layer as their main component. The inference outcomes are sent back to the mobile device. The new network architecture, including the introduced butterfly unit after a selected layer of the underlying deep model, is trained end-to-end. Our proposed method, across different wireless networks, achieves on average 53x improvements for end-to-end latency and 68 x improvements for mobile energy consumption compared to the status quo cloud-only approach for ResNet-50, while the accuracy loss is less than 2 %.

  • Smart Sensing for HVAC Control: Collaborative Intelligence in Optical and IR Cameras

    Energy management of heating, ventilation, and cooling (HVAC) has become a primary concern for residential and commercial buildings. In order to save energy without compromising the comfort of occupants, various techniques have been explored to sense the real-time occupancy/vacancy of HVAC zones. Among all these approaches, wireless-camera-based sensing stands out for its potential application in surveillance and security in addition to energy management. However, limited lifetime and detection accuracy have prevented pervasiveness of wirelesscamera-based occupancy detection. This paper presents a novel wireless device platform and prototype development that incorporates an infrared (IR) camera with an optical (OP) camera to provide collaborative intelligence at low power and enhanced accuracy. Compared to the singlesensor baseline design, the proposed fusion-based OP/IR design demonstrates 5× miss rate improvement, 5× reduction in false positive rate, and 3× lifetime extension for battery usage with respect to a single-sensor-based design. Compared to a programmed thermostat and schedulebased HVAC control, the design saves a maximum of 26% of HVAC energy.

  • Graded Concepts for Collaborative Intelligence

    Collaborative intelligence involves a combination of human and machine-based analysis, in which humans focus on higher-level tasks involving insight and understanding, whilst machines deal with gathering, filtering and processing data into a convenient and understandable form. We have proposed the use of graded concept lattices as a representation for exchanging information between machine and human in a collaborative intelligent system. Graded concepts allow summarization at multiple levels of discernibility (granularity). In this paper, we outline a new interpretation of fuzzy concept lattices as graded sets of crisp lattices. In addition, we prove equivalence between graded (fuzzy) formal concept analysis and the standard crisp framework. Consequently, any software tools developed for crisp data can be extended to the graded case without change.

  • A Generalization of Linear Positive Systems

    The dynamics of linear positive systems maps the positive orthant to itself. Namely, it maps a set of vectors with zero sign variations to itself. Hence, a natural question is: what linear systems map the set of vectors with k sign variations to itself? To address this question we use tools from the theory of cooperative dynamical systems and the theory of totally positive matrices. Our approach yields a generalization of positive linear systems called k-positive linear systems, which reduces to positive systems for k =1. We show an application of this new class of systems to the analysis of invariant sets in nonlinear time-varying dynamical systems.

  • LoBIAG: A location-based collaborative image annotation game

    One of the effective approaches for managing large amounts of image data is (semantic) annotation which on its own is considered as a difficult task for machines. To deal with this issue, leveraging humans' cognitive abilities has been become a popular trend within the recent years. Notwithstanding, when human annotators have no accurate contextual and/or location-related knowledge about the subject matter, the quality of annotation/labeling process will be faced with some challenges. Also, in some cases due to lack of such knowledge, the final results may be severely affected so that those will be unusable at all. In order to deal with the aforementioned issues and incorporating location-specific information in the process, a location-based game with the purpose of image annotation and based on the collaborative intelligence of human participants, entitled LoBIAG, is proposed. The rationale behind the work, its architecture, workflow and performance analysis are discussed in details in the paper.

  • BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services

    Recent studies have shown the latency and energy consumption of deep neural networks can be significantly improved by splitting the network between the mobile device and cloud. This paper introduces a new deep learning architecture, called BottleNet, for reducing the feature size needed to be sent to the cloud. Furthermore, we propose a training method for compensating for the potential accuracy loss due to the lossy compression of features before transmitting them to the cloud. BottleNet achieves on average 5.1× improvement in end-to-end latency and 6.9× improvement in mobile energy consumption compared to the cloud-only approach with no accuracy loss.

  • Special Issue on Service-Oriented Collaborative Computing and Applications

    The seven papers in this special section focus on the research and development of service-oriented collaborative computing technologies and their applications to the design of products, processes, systems and services in an industrial and social viewpoint.



Standards related to Collaborative Intelligence

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