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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
13 Dec 2008
TL;DR: This paper proposes a context-dependent task approach to meet the challenge, and introduces case based reasoning as the reasoning method which solves the problem to discover a task in smart home environment.
Abstract: In the future, homes will have numerous intelligent communicating devices, and such smart home needs to exhibit highly adaptive behaviors to meet the inhabitants changing personal requirements and operational context of environment. To achieve this, smart home application should focus on the inhabitant?s goal or task in diverse situations, but not the various complex devices and services. This paper proposes a context-dependent task approach to meet the challenge, and introduces case based reasoning as the reasoning method which solves the problem to discover a task in smart home environment.

1 citations

Proceedings ArticleDOI
04 Dec 2009
TL;DR: This paper proposes concepts about product ecosystems, such as the affective and cognitive dimensions and modeling techniques that need to be specified, designed and implemented in order to realize the potential of the product ecosystem vision.
Abstract: Design for products and systems that are affectively pleasurable and cognitive intuitive conveys a competitive edge in terms of customer satisfaction. Further, it is not uncommon that products tend to be correlated with one another, in conjunction with services, use environments, along with many other use context-related factors. This leads to a scenario of product ecosystem design. This paper proposes concepts about product ecosystems, such as the affective and cognitive dimensions and modeling techniques that need to be specified, designed and implemented in order to realize the potential of the product ecosystem vision. Based on timed colored Petri nets, the proposed concepts are then translated into an operative model for the design and evaluation of the product ecosystem using an illustrating example. Preliminary results shed light on the paradigm of product ecosystem design.
Book ChapterDOI
01 Jan 2021
TL;DR: Wang et al. as mentioned in this paper proposed and developed a novel framework called CrowdDeliver, which is a two-phase approach to plan package delivery paths, which exploits the existing taxi mobility to transport packages collectively (i.e., the relays among different passenger occupied taxis), without hurting the service quality to passengers too much.
Abstract: It is still difficult to make the express service profitable, despite recent years have witnessed the great demand on and attempts at the service of package express shipping. The main barrier may be due to that the speedy usually implies a higher sending frequency. To strike a trade-off between the two conflicting objectives, we propose a new idea that exploits the existing taxi mobility to transport packages collectively (i.e., the relays among different passenger-occupied taxis), without hurting the service quality to passengers too much. In more detail, we propose and develop a novel framework called CrowdDeliver, which is a two-phase approach to plan package delivery paths. In the first phase, for any give OD (i.e., Origin-Destination) pairs, we aim to identify the shortest delivery paths and also with the corresponding travel times by mining the historical taxi trajectory data offline. In the second phase, using the obtained paths and travel times as the reference to guide the adaptive path-finding, we propose an online taxi scheduling algorithm that aims to discover the near-optimal path iteratively upon the newly incoming taxi ride requests. Finally, with the large-scale taxi trajectory data collected from real life and the package delivery requests generated artificially, we conduct extensive experiments to verify the performance of CrowdDeliver. The experimental results are promising and show that more than 85% packages can be sent to their destinations within 8 h, with an average taxi relay of 4.2.
DOI
TL;DR: In this paper , a dual-mode variable optical attenuator based on the Mach-Zehnder interferometer with thermo-optic phase shifters was designed and experimentally demonstrated.
Abstract: The variable optical attenuator is one of the key components in optical communication system, but it is scarce for mode division multiplexing system. Here, we designed and experimentally demonstrated an ultra-broadband dual-mode variable optical attenuator based on Mach–Zehnder interferometer with thermo-optic phase shifters. We fabricated the device with polymer waveguide and characterized the function of the variable optical attenuator. The presented device can attenuate E11 and E12 modes simultaneously at a low power-consumption ( $\sim $ 4.8 mW) over 1500 - 1620 nm, which proves that our device is mode-insensitive and wavelength-insensitive. The presented variable optical attenuator can be widely used in mode division multiplexing system where mode-insensitive attenuating or switching is needed.
Book ChapterDOI
01 Jan 2021
TL;DR: Wang et al. as discussed by the authors presented a new type of crowdsourcing logistics to organize packages and passengers in a shared space, i.e., using a taxi that has already picked up a passenger as a package hitchhiker for on-time delivery.
Abstract: Most of current urban logistics systems do not offer a good compromise between speed and cost. Express logistics services are often associated with high shipping costs. To alleviate such contradiction, crowdsourced logistics is an encouraging solution. In this chapter, we present a new type of crowdsourcing logistics to organize packages and passengers in a shared space, i.e., using a taxi that has already picked up a passenger as a package hitchhiker for on-time delivery. Specifically, for the on-time package express service, we propose a two-stage probabilistic framework named as CrowdExpress. In the first stage, we build a package transport network by mining the historical GPS trajectory data of taxi. In the second stage, we design a taxi scheduling algorithm to dynamically find the path with the maximum probability of arriving on time based on real-time requests sent by passengers, and give the corresponding package routing. Finally, we use the real-world taxi data in the city of New York, US in a month to evaluate the system. The experiment results show that about 9500 packages are delivered successfully daily on time with a success rate of over 94%.

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