Paper introduces Privacy Mining Approach (PMA) to reveal privacy from smart homes.
problem Privacy disclosure from IoT-based smart homes for elders.
method Conducts deductions and analyses on sensor datasets to reveal privacy.
result PMA can deduce a global sensor topology and disclose elders' privacy.
Arcades uses deep learning to adapt smart-home behavior based on context.
problem Adaptive decision making in voice-controlled smart-home environments.
method Deep reinforcement learning with graphical representation of home automation system.
result Arcades promises long-term context-aware control of smart-home systems.
This paper discusses how usage patterns and preferences of inhabitants can be learned efficiently to allow smart homes to autonomously achieve energy savings. We propose a frequent sequential pattern mining algorithm suitable for real-life smart home event data. The performance of the proposed algorithm is compared to …
Federated learning improves wake word detection in smart home devices.
problem Out-of-domain issues with continuously running speech-based models.
method Federated learning with adaptive averaging strategy.
result Reduces communication rounds and upstream communication costs.
SafeAccess identifies people in smart homes for safer access.
problem Enhancing safety and independence for people with disabilities.
method Change detection, Faster-RCNN, LBP/FaceNet, MTCNN, MMS.
result Average F-score of 0.97 for identifying friends/families/caregivers versus intruders/unknown.
Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learnin…
This paper presents a case study of a recommender system that can be used to save energy in smart homes without lowering the comfort of the inhabitants. We present an algorithm that uses consumer behavior data only and uses machine learning to suggest actions for inhabitants to reduce the energy consumption of their ho…
FedRule uses graph neural networks to recommend rules for smart homes without centralizing data.
problem Manual rule setup for smart devices is inefficient and privacy-compromising.
method FedRule constructs user-specific graphs for rule recommendation, using federated learning to protect privacy.
result FedRule achieves comparable performance to centralized methods and outperforms others.
Study uses HMM for real-time activity recognition from sensor data.
problem Real-time activity recognition from streaming sensor data.
method Online hierarchical hidden Markov model.
result Improved activity recognition accuracy compared to existing methods.
Process mining is a research field focused on the analysis of event data with the aim of extracting insights in processes. Applying process mining techniques on data from smart home environments has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions. …
AutoTune learns wireless identifiers for facial recognition in real-world settings.
problem Facial recognition requires extensive user training, making it impractical for widespread deployment.
method Uses ambient wireless identifiers to train deep neural networks for facial recognition without user effort.
result Demonstrates a system that continuously refines facial recognition using wireless identifiers over time.
Paper tackles federated learning with personalised bandit algorithms.
problem Optimizing local and global objectives in a heterogeneous environment.
method Surrogate objective function combining client preferences and global knowledge; phase-based elimination algorithm.
result Achieves sublinear regret with logarithmic communication overhead.
Paper offers local neural network explanations by considering model architecture.
problem Creating true neighbourhoods for black box models.
method Penultimate layer decoding for local neighbourhood generation.
result Local explanations are more accurate and relevant to instances.
Systematic review of machine vision in robotics, highlighting challenges and improvements.
problem Challenges in machine vision for robotics, especially occlusion and lighting variance.
method Systematic literature review of 172 papers from four databases, selecting 52 relevant papers.
result Robustness and computation time improvements, but occlusion and lighting variance remain major issues.
Graph autoencoders enable ML across diverse sensor networks.
problem Deploying ML across different sensor networks with varying types or layouts.
method Graph Autoencoders for activity recognition across heterogeneous sensor networks.
result Transferable activity classifiers achieve 75% accuracy on unseen sensor layouts.
Paper generates synthetic radar signatures for motion classification.
problem Lack of large training datasets for radar-based human activity recognition.
method Adversarial learning for synthetic data generation, kinematic sifting for consistency.
result 93% overall accuracy achieved on diverse aspect angles.
Improved multi-user VoiceFilter-Lite model for speech recognition.
problem Limited performance of multi-user VoiceFilter-Lite models.
method Dual learning rate schedule and FiLM for feature conditioning.
result Closed the performance gap between multi-user and single-user VoiceFilter-Lite models.
Proposes a test for shared information between time series and events.
problem Detecting extreme events in time series data.
method Non-parametric statistical test using multiple two-sample testing at increasing lags.
result Outperforms or matches related tests on various datasets.
SA-GAN improves HAR model performance across new users.
problem Poor performance of HAR models on new user data.
method Generative Adversarial Network (GAN) for cross-subject transfer learning.
result SA-GAN outperformed other methods in HAR tasks.
Controller-Augmented Hidden Markov Models (CHMMs) are a framework for constrained sequential inference.
problem Hidden Markov models fail under pathwise constraints like precedence, visitation, or monotonic state progression.
method CHMMs compile constraints into finite-state controllers, then use standard forward-backward and Viterbi recursions to compute exact constrained posteriors and paths.
result CHMMs provide exact constrained inference, monotone ascent in constrained EM, and linear complexity in controller cardinality.
Paper presents a novel online HAR method using Hierarchical Hidden Markov Models.
problem Challenges in robust online activity recognition in smart environments.
method Two-phase approach: 1) Segmentation and activity reporting using Hierarchical Hidden Markov Models, 2) Correction of labels based on statistical features.
result Proposes a method that can detect and correct interrupted activities, outperforming state-of-the-art methods.