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Daqing Zhang

Bio: Daqing Zhang is an academic researcher from Peking University. The author has contributed to research in topics: Context (language use) & Mobile computing. The author has an hindex of 67, co-authored 331 publications receiving 16675 citations. Previous affiliations of Daqing Zhang include Institut Mines-Télécom & Institute for Infocomm Research Singapore.


Papers
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Proceedings ArticleDOI
Youwei Zeng1, Enze Yi1, Dan Wu1, Ruiyang Gao1, Daqing Zhang1 
08 Oct 2018
TL;DR: This work will demonstrate a human respiration detection system which enables full location coverage with no blind spot, and shows its potential for real-world deployment.
Abstract: In recent years, human respiration detection based on Wi-Fi signals has drawn a lot of attention due to better user acceptance and great potential for real-world deployment. However, latest studies show that respiration sensing performance varies at different locations due to the nature of Wi-Fi radio wave propagation in indoor environments, i.e., respiration detection may experience poor performance at certain locations which we call "blind spots". In this demo, we will demonstrate a human respiration detection system which enables full location coverage with no blind spot.

4 citations

Proceedings ArticleDOI
10 Sep 2007
TL;DR: A trustworthy framework that can support impromptu service discovery with mobile devices in WLAN enabled environments is proposed, which requires no specialized hardware or software installation in mobile client devices and can preserve the privacy of mobile users during the authentication process by not requesting their identities.
Abstract: Wireless hotspots are permeating our workplace, home and public places bringing interesting services and spontaneous connectivity to mobile users. While it's ideal to enable the mobile clients to discover services automatically with the device-to-hand across the augmented physical spaces, it's equally important to guarantee the security and privacy of the mobile clients and service providers. In this paper, we propose a trustworthy framework that can support impromptu service discovery with mobile devices in WLAN enabled environments. Different from the existing service discovery approaches, the framework requires no specialized hardware or software installation in mobile client devices, it can preserve the privacy of mobile users during the authentication process by not requesting their identities. We prototyped a set of assistive services in a shopping mall which can be spontaneously discovered and accessed by a disabled person from his WLAN enabled mobile device, we show how both the security and privacy are achieved in a unified manner using trusted computing platforms.

4 citations

Book ChapterDOI
29 Jun 2009
TL;DR: A context-dependent task approach to manage the pervasive services, and the case based reasoning (CBR) method has been adopted and implemented to recognize tasks, enabling task-oriented system design in smart home environments.
Abstract: Smart Home is a hot research area that has gained a lot of attention in recent years. Smart home applications should focus on the inhabitant's goal or task in diverse situations, rather than the various complex devices and services. One of the important issues for Smart Home design is to perceive the environment and assess occurring situations, thus allowing systems to behave intelligently. This paper proposes a context-dependent task approach to manage the pervasive services, the case based reasoning (CBR) method has been adopted and implemented to recognize tasks, enabling task-oriented system design in smart home environments.

4 citations

Proceedings ArticleDOI
05 Dec 2005
TL;DR: An ontology based model is developed to integrate both context and content in one unifying framework to facilitate the creation of location-enhanced, user-centric vehicular telematic services.
Abstract: By incorporating personalised content as context, vehicular telematic services can be made more user-centric, as opposed to vehicle-centric. We develop an ontology based model to integrate both context and content in one unifying framework to facilitate the creation of such services. Scenes captured spontaneously by the user can be a useful index to his or her intent and interests. Our empirical study shows that location-con text leads to a higher precision in recognizing these captured scenes. A "tourist information" prototype based on scene recognition is built, to illustrate one such location-enhanced, user-centric service.

4 citations

Journal ArticleDOI
TL;DR: This paper proposes an autonomic system for activity scheduling in MoSoN communities that allows flexible activity proposition while efficiently handling the user conflicts, and can schedule multiple simultaneous activities in real-time while incurring low message and time cost.
Abstract: Mobile social network (MoSoN) signifies an emerging area in the social computing research built on top of the mobile communications and wireless networking. It allows virtual community formation among like minded users to share data and to organize collaborative social activities at commonly agreed upon places and times. Such an activity scheduling in real-time is non-trivial as it requires tracing multiple users’ profiles, preferences, and other spatio-temporal contexts, like location, and availability. Inherent conflicts among users regarding choices of places and time slots further complicates unanimous decision making. In this paper, we propose an autonomic system for activity scheduling in MoSoN communities. Our system allows flexible activity proposition while efficiently handling the user conflicts. As evident from our simulation and testbed results and analysis, our system can schedule multiple simultaneous activities in real-time while incurring low message and time cost.

3 citations


Cited by
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01 Jan 2002

9,314 citations

Journal ArticleDOI
TL;DR: This paper surveys context awareness from an IoT perspective and addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT.
Abstract: As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

2,542 citations

Proceedings ArticleDOI
22 May 2017
TL;DR: This work quantitatively investigates how machine learning models leak information about the individual data records on which they were trained and empirically evaluates the inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon.
Abstract: We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training dataset. To perform membership inference against a target model, we make adversarial use of machine learning and train our own inference model to recognize differences in the target model's predictions on the inputs that it trained on versus the inputs that it did not train on. We empirically evaluate our inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon. Using realistic datasets and classification tasks, including a hospital discharge dataset whose membership is sensitive from the privacy perspective, we show that these models can be vulnerable to membership inference attacks. We then investigate the factors that influence this leakage and evaluate mitigation strategies.

2,059 citations