Arcades: A deep model for adaptive decision making in voice controlled smart-home

Brenon, Alexis; Portet, François; Vacher, Michel

doi:10.1016/j.pmcj.2018.06.011

Cited by 26 publications

(14 citation statements)

References 44 publications

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“…The first are requirements. From functional requirements to emotional needs, methods such as user surveys are adopted to collect user data accurately to clarify the real needs of users [11,34,47]. The second is user participation.…”

Section: Discussionmentioning

confidence: 99%

“…(2) The elderly or users with speech defects may encounter a series of problems in the home environment. The special speech recognition system developed can help them solve speech disorders and ensure their safety and health in life [33][34][35][36][37]. (3) In order to satisfy the users in the long distance voice interaction under special environment requirements, the designers of the analysis of the intelligent building system in the process of long distance voice interaction in which may exist problems, put forward the speech recognition system for long distances, and broke the voice interaction possible distance limit, to improve the user experience in the intelligence environment [38][39][40][41][42][43][44].…”

Section: The Voice Interactionmentioning

confidence: 99%

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A Review of Smart Design Based on Interactive Experience in Building Systems

Zhang

et al. 2020

Sustainability

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Smart building is the result of the penetration of information technology and control technology into traditional buildings, and is the future development direction of buildings. User-centric building smart design can achieve a sustainable life, and smart technology integration based on smart design can support sustainable development and improve user satisfaction, happiness, and overall quality of life. In intelligent design, researchers focus on the “people-oriented” approach, designed to bring users the ultimate interactive experience. Based on the interactive experience principle of smart design in the smart building system, this article classifies and summarizes intelligent design from the “five senses” interaction, including visual interaction, voice interaction, tactile interaction, cognitive interaction, and emotional interaction. We analyze the application of smart design in architecture and discuss how to embody the principles of user-centered interactive experience design in the process of smart design. This article provides a comprehensive and systematic literature review, clarifies the importance of the “people-oriented” approach in the smart design of buildings, and summarizes how to improve the sustainability of intelligent buildings from the perspective of a “people-oriented” approach. This paper proposes future research suggestions and directions and promotes the sustainable development of the smart building.

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Section: Discussionmentioning

confidence: 99%

Section: The Voice Interactionmentioning

confidence: 99%

A Review of Smart Design Based on Interactive Experience in Building Systems

Zhang

et al. 2020

Sustainability

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“…Although these results add credence to the interest of federated learning for pervasive computing, a lot of challenges still remain. The study must be replicated with more datasets and different tasks [39]. We also plan to study the robustness of FL in scenarios such as asynchronous learning (devices come and go), a sudden change in client data, communication issues, heterogeneous population of devices (e.g., traveling device), and mismatches between server data and clients (noisy acquisition).…”

Section: Conclusion and Further Workmentioning

confidence: 99%

A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison

Portet

Lalanda

et al. 2021

2021 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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Pervasive computing promotes the installation of connected devices in our living spaces in order to provide services. Two major developments have gained significant momentum recently: an advanced use of edge resources and the integration of machine learning techniques for engineering applications. This evolution raises major challenges, in particular related to the appropriate distribution of computing elements along an edgeto-cloud continuum. About this, Federated Learning has been recently proposed for distributed model training in the edge. The principle of this approach is to aggregate models learned on distributed clients in order to obtain a new, more general model. The resulting model is then redistributed to clients for further training. To date, the most popular federated learning algorithm uses coordinate-wise averaging of the model parameters for aggregation. However, it has been shown that this method is not adapted in heterogeneous environments where data is not identically and independently distributed (non-iid). This corresponds directly to some pervasive computing scenarios where heterogeneity of devices and users challenges machine learning with the double objective of generalization and personalization. In this paper, we propose a novel aggregation algorithm, termed FedDist, which is able to modify its model architecture (here, deep neural network) by identifying dissimilarities between specific neurons amongst the clients. This permits to account for clients' specificity without impairing generalization. Furthermore, we define a complete method to evaluate federated learning in a realistic way taking generalization and personalization into account.Using this method, FedDist is extensively tested and compared with three state-of-the-art federated learning algorithms on the pervasive domain of Human Activity Recognition with smartphones.

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“…Human activity recognition in smart homes improves the residents' quality of life and provides independence and allows the residents to live independently in safety and comfort in their homes. [80][81][82] Different deep learning models such as the Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) have been used to address the human pose recognition and the Human Activity Recognition (HAR) task using, for example, convolutions across two or three dimensions to capture an image's spatial patterns. Additionally, the optimal operation of smart home appliances allows residents to take advantage of time-of-use pricing scheme to reduce costs (eg, their electricity costs [83][84][85].…”

Section: Smart Homesmentioning

confidence: 99%

Deep learning support for intelligent transportation systems

Ibáñez

Contreras-Castillo

Zeadally

2020

Trans Emerging Tel Tech

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Intelligent Transportation Systems (ITS) help improve the ever-increasing vehicular flow and traffic efficiency in urban traffic to reduce the number of accidents. The generation of massive amounts of data generated by all the digital devices connected to the transportation network enables the creation of datasets to perform an in-depth analysis of the data using deep learning methods. Such methods can help predict traffic performance, automated traffic light management, lane detection, and identifying objects near vehicles to increase the safety and efficiency of ITS. We discuss some of the challenges that need to be solved to achieve seamless integration between ITS and deep learning methods to address issues such as (1) improving traffic flow/transportation logistics, (2) predicting best routes for the transportation of goods, (3) optimal fuel consumption, (4) intelligent environmental conditions perception, (5) traffic speed management, and accident prevention.

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Arcades: A deep model for adaptive decision making in voice controlled smart-home

Cited by 26 publications

References 44 publications

A Review of Smart Design Based on Interactive Experience in Building Systems

A Review of Smart Design Based on Interactive Experience in Building Systems

A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison

Deep learning support for intelligent transportation systems

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