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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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Journal ArticleDOI
TL;DR: This paper proposes a novel worker selection framework, named WSelector, to more precisely select appropriate workers by taking various contexts into account and demonstrates the expressiveness of the framework by implementing multiple crowd-sensing tasks and evaluates the effectiveness of the case-based reasoning algorithm for willingness-based selection.
Abstract: Worker selection for many crowd-sensing tasks must consider various complex contexts to ensure high quality of data. Existing platforms and frameworks take only specific contexts into account to demonstrate motivating scenarios but do not provide general context models or frameworks in support of crowd-sensing at large. This paper proposes a novel worker selection framework, named WSelector, to more precisely select appropriate workers by taking various contexts into account. To achieve this goal, it first provides programming time support to help task creator define constraints. Then its runtime system adopts a two-phase process to select workers who are not only qualified but also more likely to undertake a crowd-sensing task. In the first phase, it selects workers who satisfy predefined constraints. In the second phase, by leveraging the worker’s past participation history, it further selects those who are more likely to undertake a crowd-sensing task based on a case-based reasoning algorithm. We demonstrate the expressiveness of the framework by implementing multiple crowd-sensing tasks and evaluate the effectiveness of the case-based reasoning algorithm for willingness-based selection by using a questionnaire-generated dataset. Results show that our case-based reasoning algorithm outperforms the currently practiced baseline method.

7 citations

Proceedings ArticleDOI
Zhu Wang, Daqing Zhang1, Dingqi Yang1, Zhiyong Yu1, Xingshe Zhou, Zhiwen Yu 
01 Nov 2012
TL;DR: Based on the user-venue check-in relationship and user/venue attributes, and based on the rich metadata of users and venues, a quantitative community profiling mechanism is put forward to indicate the preferences, interests and habits of a community.
Abstract: While the detection of social subgroups (i.e., communities) has always been a fundamental task in social network analysis, few efforts has been made to characterize the detected community. Meanwhile, to effectively facilitate applications based on the community structure, it is very important to understand the features of each community. Thereby, a systematic community profiling mechanism is needed. With the recent surge of location-based social networks (LBSNs, e.g., Foursquare, Facebook Places), huge amount of digital footprints about users' locations, profiles as well as their online social connections provide sufficient metadata for community profiling. Different from social networks (e.g., Flickr, Facebook) which have explicit groups for users to subscribe or join, LBSNs usually have no explicit community structure. In order to capitalize on the large number of potential users, quality community detection and profiling approaches are needed so as to enable applications such as direct marketing, group tracking, etc. In this paper, based on the user-venue check-in relationship and user/venue attributes, we come out with a novel community profiling framework. Specifically, we first adopt edge-clustering to simultaneously group both users and venues into communities, and then based on the rich metadata of users and venues we put forward a quantitative community profiling mechanism to indicate the preferences, interests and habits of a community. The efficacy of our approach is validated by intensive empirical evaluations using the collected Foursquare dataset of 266,838 users with 9,803,764 check-ins over 2,477,122 venues worldwide.

7 citations

Proceedings ArticleDOI
08 Oct 2018
TL;DR: A non-intrusive system WiFit is shown in this demo which uses surrounding Wi-Fi signals to monitor the bodyweight exercises without any attachment requirements and could recognize the exercise type and count the repetition number of exercise for diverse population even in different environments.
Abstract: Bodyweight exercises, such as push-up, sit-up, and squat, are effective forms of strength training to maintain good health. In order to improve people's exercise experience and provide feedback, lots of work has been done to monitor the bodyweight exercise by requiring people to wear special sensors on body. Different from traditional ways, a non-intrusive system WiFit is shown in this demo which uses surrounding Wi-Fi signals to monitor the bodyweight exercises without any attachment requirements. It not only could recognize the exercise type but also count the repetition number of exercise for diverse population even in different environments.

7 citations

Journal ArticleDOI
28 Sep 2021
TL;DR: This paper retrospects two general-purpose sensing models, i.e., the Fresnel zone model and CSI-ratio model, and demonstrates how these two models are leveraged to extract insightful properties and support a variety of device-free sensing applications.
Abstract: Over the past decade, WiFi CSI-based device-free sensing technology has shown great potential in smart homes, assisted living, and many other applications. While model-based device-free sensing approaches analyze and recognize human behaviors by constructing mathematical relationships among WiFi devices, environment, human position/posture, and received channel state information, they have attracted great attention because of the interpretable physical meaning and the ability to guide the WiFi-based sensing system design. In this paper, we retrospect two general-purpose sensing models, i.e., the Fresnel zone model and CSI-ratio model, and demonstrate how these two models are leveraged to extract insightful properties and support a variety of device-free sensing applications.

7 citations

Journal ArticleDOI
TL;DR: An overview of new retail, which leverages wireless sensing and machine learning techniques to recognize fine-grained in-store customer behaviors, infer their intents, and learn their preferences is given.
Abstract: In recent years, we have witnessed a surge in new retail, which aims to combine the best of physical and online retailing using Internet of things and artificial intelligence techniques. The unmanned store is a representative type of new retail, which leverages wireless sensing and machine learning techniques to recognize fine-grained in-store customer behaviors, infer their intents, and learn their preferences. This paper gives an overview of this emerging research area, presents its key techniques and applications, and discusses the open issues of this field.

7 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