Context Aware Systems, Methods and Trends in Smart Home Technology

Robles, Rosslin John; Kim, Tai-hoon

doi:10.1007/978-3-642-16444-6_20

Cited by 24 publications

(30 citation statements)

References 1 publication

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“…However, Adam and Spedding (2007) found that users would be open to being monitored if it will "significantly help them." To achieve this, SHT must be made context-aware, meaning that the device can understand a user's current situation by sensing and reacting based on their environment (Robles & Kim, 2010). When a device is context-aware, the device might know when to start and stop tracking its residents.…”

Section: Designing Useful Trustable and Secure Smart Home Devicesmentioning

confidence: 99%

“…Smart home technology are devices that connect to the internet to automate home living. A smart home is a home environment that consists of various electronic devices that improve the quality of living through collecting and analyzing data from its residents (Mocrii et al, 2018;Robles & Kim, 2010;Silva, Morikawa, & Petra, 2012). Despite the growing popularity of this technology, the global adoption rate remains low, with only 7.5% of households adopting SHT, generating around $46 billion in 2018 so far (Shin, Park, & Lee, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Smart Home Devices: Promoting User Trust and Protecting User Data

Goh

Nathan-Roberts

2019

Proceedings of the Human Factors and Ergonomics Society Annual

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As smart technology is introduced into our homes, new dangers emerge that threaten our safety. New technology is usually subjected to much scrutiny, but smart home devices face even more because they are brought into the home environment, which is focused on safety and privacy. The potential for smart home technology to improve home life is hindered by the fact that potential users face difficulty in trusting and accepting smart home technology. This paper explores different types of trust that can be used to inform strategies, promote trust, reduce threats towards smart home technologies, and overcome challenges in designing these systems and different methods for designing a trustworthy and secure system. To begin designing a trustworthy product that establishes trust between users and smart home technology, manufacturers should use these findings to understand how human beings form trust with new technology.

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Section: Designing Useful Trustable and Secure Smart Home Devicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smart Home Devices: Promoting User Trust and Protecting User Data

Goh

Nathan-Roberts

2019

Proceedings of the Human Factors and Ergonomics Society Annual

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“…У найпростішому випадку вона повинна вміти розпізнавати конкретні ситуації, що відбува-ються в будинку, і відповідним чином на них реагу-вати: одна з систем може управляти поведінкою інших за заздалегідь виробленим алгоритмом. Ін-шими словами, це будівля, інженерні системи якої здатні забезпечити адаптацію до можливих змін в майбутньому [1].…”

Section: вступunclassified

Застосування Телекомунікаційних Технологій В Структурі «розумного Будинку»

Кравчук¹,

Міночкін²,

Кайденко³

2017

СОІ

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“…Machine learning and pattern recognition, including classification, are perhaps at the peak of their development with a sharp interest not only from scientists and practitioners, but also from the wider public and media. This is, in part, thanks to the boom surrounding the wider area of artificial intelligence (AI) and recent successful and widely publicized applications ranging from games [14,34], driverless cars [10,33], defense and security [1,32,35], home applications [23,28]. Despite the great success of the standard bearer algorithm in this area, the so-called deep learning in image and speech recognition [18,27], the underlying concept of machine learning which requires large amount of labeled training data remains unchanged.…”

Section: Introductionmentioning

confidence: 99%

Detecting and learning from unknown by extremely weak supervision: exploratory classifier (xClass)

Angelov

Soares

2021

Neural Comput & Applic

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In this paper, we break with the traditional approach to classification, which is regarded as a form of supervised learning. We offer a method and algorithm, which make possible fully autonomous (unsupervised) detection of new classes, and learning following a very parsimonious training priming (few labeled data samples only). Moreover, new unknown classes may appear at a later stage and the proposed xClass method and algorithm are able to successfully discover this and learn from the data autonomously. Furthermore, the features (inputs to the classifier) are automatically sub-selected by the algorithm based on the accumulated data density per feature per class. In addition, the automatically generated model is easy to interpret and is locally generative and based on prototypes which define the modes of the data distribution. As a result, a highly efficient, lean, human-understandable, autonomously self-learning model (which only needs an extremely parsimonious priming) emerges from the data. To validate our proposal, we approbated it on four challenging problems, including imbalanced Faces-1999 data base, Caltech-101 dataset, vehicles dataset, and iRoads dataset, which is a dataset of images of autonomous driving scenarios. Not only we achieved higher precision (in one of the problems outperforming by 25% all other methods), but, more significantly, we only used a single class beforehand, while other methods used all the available classes and we generated interpretable models with smaller number of features used, through extremely weak and weak supervision. We demonstrated the ability to detect and learn new classes for both images and numerical examples.

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Context Aware Systems, Methods and Trends in Smart Home Technology

Cited by 24 publications

References 1 publication

Smart Home Devices: Promoting User Trust and Protecting User Data

Smart Home Devices: Promoting User Trust and Protecting User Data

Застосування Телекомунікаційних Технологій В Структурі «розумного Будинку»

Detecting and learning from unknown by extremely weak supervision: exploratory classifier (xClass)

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