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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
07 Sep 2015
TL;DR: This paper proposes a novel Credit Distribution-User Influence Preference (CD-UIP) algorithm to find the most influential and preferable followers as the invitees in EBSNs, and demonstrates the proposed algorithm outperforms the state-of-the-art prediction methods.
Abstract: The newly emerging event-based social networks (EBSNs) extend social interaction from online to offline, providing an appealing platform for people to organize and participate realworld social events. In this paper, we investigate how to select potential participants in EBSNs from an event host's point of view. We formulate the problem as mining influential and preferable invitee set, considering from two complementary aspects. The first aspect concerns users' preference with respect to the event. The second aspect is influence maximization, which aims to influence the largest number of users to participate the event. In particular, we propose a novel Credit Distribution-User Influence Preference (CD-UIP) algorithm to find the most influential and preferable followers as the invitees. We collect a real-world dataset from a popular EBSNs called "Douban Events", and the experimental results on the dataset demonstrate the proposed algorithm outperforms the state-of-the-art prediction methods.

34 citations

Journal ArticleDOI
TL;DR: Treasure exploits the “design-in-play” concept to enhance the variability of a game in mixed-reality environments and dynamic and personalized role design and allocation by players is enabled by exploring local smart objects as game props.
Abstract: Treasure is a pervasive game playing in the context of people's daily living environments. Unlike previous pervasive games that are based on the predefined contents and proprietary devices, Treasure exploits the "design-in-play" concept to enhance the variability of a game in mixed-reality environments. Dynamic and personalized role design and allocation by players is enabled by exploring local smart objects as game props. The variability of the game is also enhanced by several other aspects, such as user-oriented context-aware action setting and playing environment redeployment. The effectiveness of the "design-in-play" concept is validated through a user study, where 15 subjects were recruited to play and author the trial game.

33 citations

Proceedings ArticleDOI
04 Sep 2012
TL;DR: This paper presents hybrid social networking (HSN), which highlights the interweaving and cooperation of heterogeneous communities, and demonstrates how HSN augments information dissemination in human daily life.
Abstract: In modern life, people are involved in multiple online and offline social communities. Previous works on content sharing and information dissemination are single-community oriented. The complementary features as well as the joint-effect of distinct forms of social communities, however, has not been well explored. In this paper, we present hybrid social networking (HSN), which highlights the interweaving and cooperation of heterogeneous communities. In particular, we present and demonstrate how HSN augments information dissemination in human daily life. The infrastructure, community creation and cross-community information dissemination algorithms, and a use case of HSN are presented. A real mobility trace of 104 users is employed to validate the performance of HSN, in comparison with single-community dependent methods.

33 citations

Journal ArticleDOI
TL;DR: This paper performs an extensive literature review of learning-assisted optimization approaches in MCS, and presents different learning and optimization methods, and discusses how different techniques can be combined to form a complete solution.
Abstract: Mobile crowd sensing (MCS) is a relatively new paradigm for collecting real-time and location-dependent urban sensing data Given its applications, it is crucial to optimize the MCS process with the objective of maximizing the sensing quality and minimizing the sensing cost While earlier studies mainly tackle this issue by designing different combinatorial optimization algorithms, there is a new trend to further optimize MCS by integrating learning techniques to extract knowledge, such as participants’ behavioral patterns or sensing data correlation In this paper, we perform an extensive literature review of learning-assisted optimization approaches in MCS Specifically, from the perspective of the participant and the task, we organize the existing work into a conceptual framework, present different learning and optimization methods, and describe their evaluation Furthermore, we discuss how different techniques can be combined to form a complete solution In the end, we point out existing limitations, which can inform and guide future research directions

33 citations

Journal ArticleDOI
TL;DR: Evaluation results show that ecoSense could reduce total 3G data cost by up to up to $ {\sim }50$ %, when compared to the direct-assignment method that assigns each participant to UnDP or PAYG directly according to the size of her sensed data.
Abstract: In mobile crowdsensing (MCS), one of the participants’ main concerns is the cost for 3G data usage, which affects their willingness to participate in a crowdsensing task. In this paper, we present the design and implementation of an MCS data uploading mechanism—ecoSense—to help reduce additional 3G data cost incurred by the whole crowd of sensing participants. By considering the two most common real-life 3G price plans—unlimited data plan (UnDP) and pay as you go (PAYG), ecoSense partitions all the users into two groups corresponding to these two price plans at the beginning of each month, with the objective of minimizing the total refunding budget for all participants. The partitioning is based on predicting users’ mobility patterns and sensed data size. The ecoSense mechanism is designed inspired by the observation that during the data uploading cycles, UnDP users could opportunistically relay PAYG users’ data to the crowdsensing server without extra 3G cost, provided the two types of users are able to “meet” on a common local cost-free network (e.g., Bluetooth or WiFi direct). We conduct our experiments using both the Massachusetts Institute of Technology reality mining and the Small World In Motion (SWIM) simulation data sets. Evaluation results show that ecoSense could reduce total 3G data cost by up to $ {\sim }50$ %, when compared to the direct-assignment method that assigns each participant to UnDP or PAYG directly according to the size of her sensed data.

33 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