An official website of the United States government
The success and impact of activity recognition algorithms largely depends on the availability of the labeled training samples and adaptability of activity recognition models across various domains. In a new environment, the pre-trained activity recognition models face challenges in presence of sensing bias- ness, device heterogeneities, and inherent variabilities in human behaviors and activities. Activity Recognition (AR) system built in one environment does not scale well in another environment, if it has to learn new activities and the annotated activity samples are scarce. Indeed building a new activity recognition model and training the model with large annotated samples often help overcome this challenging problem. However, collecting annotated samples is cost-sensitive and learning activity model at wild is computationally expensive. In this work, we propose an activity recognition framework, UnTran that utilizes source domains' pre-trained autoencoder enabled activity model that transfers two layers of this network to generate a common feature space for both source and target domain activities. We postulate a hybrid AR framework that helps fuse the decisions from a trained model in source domain and two activity models (raw and deep-feature based activity model) in target domain reducing the demand of annotated activity samples to help recognize unseen activities. We evaluated our framework with three real-world data traces consisting of 41 users and 26 activities in total. Our proposed UnTran AR framework achieves ≈ 75% F1 score in recognizing unseen new activities using only 10% labeled activity data in the target domain. UnTran attains ≈ 98% F1 score while recognizing seen activities in presence of only 2-3% of labeled activity samples.
more »
« less
Scaling Human Activity Recognition via Deep Learning-based Domain Adaptation
We investigate the problem of making human activity recognition (AR) scalable-i.e., allowing AR classifiers trained in one context to be readily adapted to a different contextual domain. This is important because AR technologies can achieve high accuracy if the classifiers are trained for a specific individual or device, but show significant degradation when the same classifier is applied context-e.g., to a different device located at a different on-body position. To allow such adaptation without requiring the onerous step of collecting large volumes of labeled training data in the target domain, we proposed a transductive transfer learning model that is specifically tuned to the properties of convolutional neural networks (CNNs). Our model, called HDCNN, assumes that the relative distribution of weights in the different CNN layers will remain invariant, as long as the set of activities being monitored does not change. Evaluation on real-world data shows that HDCNN is able to achieve high accuracy even without any labeled training data in the target domain, and offers even higher accuracy (significantly outperforming competitive shallow and deep classifiers) when even a modest amount of labeled training data is available.
more »
« less
- Award ID(s):
- 1750936
- PAR ID:
- 10087463
- Date Published:
- Journal Name:
- 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom)
- Page Range / eLocation ID:
- 1 to 9
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Huge amounts of data generated on social media during emergency situations is regarded as a trove of critical information. The use of supervised machine learning techniques in the early stages of a crisis is challenged by the lack of labeled data for that event. Furthermore, supervised models trained on labeled data from a prior crisis may not produce accurate results, due to inherent crisis variations. To address these challenges, the authors propose a hybrid feature-instance-parameter adaptation approach based on matrix factorization, k-nearest neighbors, and self-training. The proposed feature-instance adaptation selects a subset of the source crisis data that is representative for the target crisis data. The selected labeled source data, together with unlabeled target data, are used to learn self-training domain adaptation classifiers for the target crisis. Experimental results have shown that overall the hybrid domain adaptation classifiers perform better than the supervised classifiers learned from the original source data.more » « less
-
Modern machine learning underpins a large variety of commercial software products, including many cybersecurity solutions. Widely different models, from large transformers trained for auto-regressive natural language modeling to gradient boosting forests designed to recognize malicious software, all share a common element: they are trained on an ever increasing quantity of data to achieve impressive performance levels in their tasks. Consequently, the training phase of modern machine learning systems holds dual significance: it is pivotal in achieving the expected high-performance levels of these models, and concurrently, it presents a prime attack surface for adversaries striving to manipulate the behavior of the final trained system. This dissertation explores the complexities and hidden dangers of training supervised machine learning models in an adversarial setting, with a particular focus on models designed for cybersecurity tasks. Guided by the belief that an accurate understanding of the offensive capabilities of the adversary is the cornerstone on which to found any successful defensive strategy, the bulk of this thesis is composed by the introduction of novel training-time attacks. We start by proposing training-time attack strategies that operate in a clean-label regime, requiring minimal adversarial control over the training process, allowing the attacker to subvert the victim model’s prediction through simple poisoned data dissemination. Leveraging the characteristics of the data domain and model explanation techniques, we craft training data perturbations that stealthily subvert malicious software classifiers. We then shift the focus of our analysis on the long-standing problem of network flow traffic classification. In this context we develop new poisoning strategies that work around the constraints of the data domain through different strategies, including generative modeling. Finally, we examine unusual attack vectors, when the adversary is capable of tampering with different elements of the training process, such as the network connections during a federated learning protocol. We show that such an attacker can induce targeted performance degradation through strategic network interference, while maintaining stable the performance of the victim model on other data instances. We conclude by investigating mitigation techniques designed to target these insidious clean-label backdoor attacks in the cybersecurity domain.more » « less
-
Recent advances in eye tracking have given birth to a new genre of gaze-based context sensing applications, ranging from cognitive load estimation to emotion recognition. To achieve state-of-the-art recognition accuracy, a large-scale, labeled eye movement dataset is needed to train deep learning-based classifiers. However, due to the heterogeneity in human visual behavior, as well as the labor-intensive and privacy-compromising data collection process, datasets for gaze-based activity recognition are scarce and hard to collect. To alleviate the sparse gaze data problem, we present EyeSyn, a novel suite of psychology-inspired generative models that leverages only publicly available images and videos to synthesize a realistic and arbitrarily large eye movement dataset. Taking gaze-based museum activity recognition as a case study, our evaluation demonstrates that EyeSyn can not only replicate the distinct pat-terns in the actual gaze signals that are captured by an eye tracking device, but also simulate the signal diversity that results from different measurement setups and subject heterogeneity. Moreover, in the few-shot learning scenario, EyeSyn can be readily incorporated with either transfer learning or meta-learning to achieve 90% accuracy, without the need for a large-scale dataset for training.more » « less
-
Activity Recognition (AR) models perform well with a large number of available training instances. However, in the presence of sensor heterogeneity, sensing biasness and variability of human behaviors and activities and unseen activity classes pose key challenges to adopting and scaling these pre-trained activity recognition models in the new environment. These challenging unseen activities recognition problems are addressed by applying transfer learning techniques that leverage a limited number of annotated samples and utilize the inherent structural patterns among activities within and across the source and target domains. This work proposes a novel AR framework that uses the pre-trained deep autoencoder model and generates features from source and target activity samples. Furthermore, this AR frame-work establishes correlations among activities between the source and target domain by exploiting intra- and inter-class knowledge transfer to mitigate the number of labeled samples and recognize unseen activities in the target domain. We validated the efficacy and effectiveness of our AR framework with three real-world data traces (Daily and Sports, Opportunistic, and Wisdm) that contain 41 users and 26 activities in total. Our AR framework achieves performance gains ≈ 5-6% with 111, 18, and 70 activity samples (20 % annotated samples) for Das, Opp, and Wisdm datasets. In addition, our proposed AR framework requires 56, 8, and 35 fewer activity samples (10% fewer annotated examples) for Das, Opp, and Wisdm, respectively, compared to the state-of-the-art Untran model.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/PERCOM.2018.8444585
