Lisha Hu

Bio: Lisha Hu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Activity recognition & Wearable technology. The author has an hindex of 12, co-authored 23 publications receiving 1155 citations. Previous affiliations of Lisha Hu include Hebei University of Economics and Business.

ContextSense: unobtrusive discovery of incremental social context using dynamic bluetooth data

Abstract: User-centric ambient social contexts can be effectively captured by dynamic bluetooth data. However, conventional approaches for training classifiers struggle with social contexts that are incrementally constructed and continuously discovered in everyday environments. Incremental social contexts can confuse a classifier because it assumes that the number and composition of context classes is fixed throughout training and inference phases. To address this challenge we propose ContextSense, an ELM-based learning method for continuous and unobtrusive discovery of new social contexts incrementally from dynamic bluetooth data. Experimental results show that ContextSense can automatically cope with "incremental social context" classes that appear unpredictably in the real-world.

Unobtrusive Sensing Incremental Social Contexts Using Class Incremental Learning

Abstract: By utilizing captured characteristics of surrounding contexts through widely used Bluetooth sensor, user-centric social contexts can be effectively sensed and discovered by dynamic Bluetooth information. At present, state-of-the-art approaches for building classifiers can basically recognize limited classes trained in the learning phase; however, due to the complex diversity of social contextual behavior, the built classifier seldom deals with newly appeared contexts, which results in degrading the recognition performance greatly. To address this problem, we propose, an OSELM (online sequential extreme learning machine) based class incremental learning method for continuous and unobtrusive sensing new classes of social contexts from dynamic Bluetooth data alone. We integrate fuzzy clustering technique and OSELM to discover and recognize social contextual behaviors by real-world Bluetooth sensor data. Experimental results show that our method can automatically cope with incremental classes of social contexts that appear unpredictably in the real-world. Further, our proposed method have the effective recognition capability for both original known classes and newly appeared unknown classes, respectively.

A Novel Feature Incremental Learning Method for Sensor-Based Activity Recognition

Abstract: Recognizing activities of daily living is an important research topic for health monitoring and elderly care. However, most existing activity recognition models only work with static and pre-defined sensor configurations. Enabling an existing activity recognition model to adapt to the emergence of new sensors in a dynamic environment is a significant challenge. In this paper, we propose a novel feature incremental learning method, namely the Feature Incremental Random Forest (FIRF), to improve the performance of an existing model with a small amount of data on newly appeared features. It consists of two important components – 1) a mutual information based diversity generation strategy (MIDGS) and 2) a feature incremental tree growing mechanism (FITGM). MIDGS enhances the internal diversity of random forests, while FITGM improves the accuracy of individual decision trees. To evaluate the performance of FIRF, we conduct extensive experiments on three well-known public datasets for activity recognition. Experimental results demonstrate that FIRF is significantly more accurate and efficient compared with other state-of-the-art methods. It has the potential to allow the dynamic exploitation of new sensors in changing environments.

Deep learning for time series classification: a review

Abstract: Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.

Deep Learning in Mobile and Wireless Networking: A Survey

Abstract: The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Abstract: In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.

The Rise of Consumer Health Wearables: Promises and Barriers

Abstract: Lukasz Piwek and colleagues consider whether wearable technology can become a valuable asset for health care.