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.

GeeAir: Waving in the Air to Control Home Appliances

Abstract: This video is a demonstration of our previous work [5], where we presented a handheld mobile device Gee Air, a gesture-based universal controller for home appliances Combining the speech, gesture, joystick, button, and light, Gee Air allows the users to interact with the appliances via a multi-modal means The users can control varied home appliances just by waving the Gee Air in the air The design of Gee Air is better than the existing universal remote controllers in that it satisfies the requirement of different users group, eg physically disabled and vision-impaired users

Automatic Event Scheduling in Mobile Social Network Communities

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, availability, etc. 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 results and analysis, our system can schedule multiple simultaneous activities in real-time while incurring low message and time cost.

Mode-selective modulator and switch based on graphene-polymer hybrid waveguides.

Abstract: The mode-division multiplexing (MDM) is an effective technology with huge development potential to improve the transmission capacity of optical communication system by transmitting multiple modes simultaneously in a few-mode fiber. In traditional MDM technology, the fundamental modes of multiple channels are usually modulated by external individual arranged electro-optic modulators, and then multiplexed into the few-mode fiber or waveguide by a mode multiplexer. However, this is usually limited by large device footprint and high power consumption. Here, we report a mode-selective modulator and switch to individually modulate or switch the TE11, TE12 and TE21 modes in a few-mode waveguide (FMW) to overcome this limitation. Our method is based on the graphene-polymer hybrid platform with four graphene capacitors buried in different locations of the polymer FMW by utilizing the coplanar interaction between the capacitors and spatial modes. The TE11, TE12 and TE21 modes in the FMW can be modulated and switched separately or simultaneously by applying independent gate voltage to different graphene capacitor of the device. Our study is expected to make the selective management of the spatial modes in MDM transmission systems more flexible.

Context Aware Computing for The Internet of Things: A Survey

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.

Membership Inference Attacks Against Machine Learning Models

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.